key: cord-1018720-5dyq3gb9 authors: Liu, Yuan; Yu, Yangyang; Zhao, Yanji; He, Daihai title: Reduction in the infection fatality rate of Omicron variant compared to previous variants in South Africa date: 2022-04-21 journal: Int J Infect Dis DOI: 10.1016/j.ijid.2022.04.029 sha: 77a9241f156e2436b0a5f07a656d6e1a3567717b doc_id: 1018720 cord_uid: 5dyq3gb9 Objective Omicron (B.1.1.529) variant has caused global concern. Previous studies have shown that the variant has enhanced immune evasion ability and transmissibility, and reduced severity. Methods In this work, we develop a mathematical model with time-varying transmission rate, vaccination and immune evasion. We fit model to reported case and death data, up to Feb 6, 2022 to estimate the transmissibility and infection fatality ratio of Omicron variant in South Africa. Results We found that the high relative transmissibility of Omicron variant is mainly due to its immune evasion ability while the infection fatality rate is substantially decreased. The reduction in the infection fatality rate is about 78.7% (95% confidence interval: 66.9%, 85%). Conclusion Based on data in South Africa and mathematical modelling, we found that the Omicron variant is highly transmissible but with significantly lower infection fatality rate compared to previous variant of SARS-COV-2. The coronavirus pandemic has been going on for nearly two years since 2019. According to the WHO, there were over 260 million cases including more than 5 million deaths reported ('https://www.who.int/emergencies/diseases/novel-coronavirus-2019' 2021). The virus, first identified in late 2019, has mutated multiple times and has been classified by the WHO into three categories: variants of concern(VOC), variants of interest (VOI) and variants under monitoring (VUM). As four of the variants of concern which includes Alpha variant was designated as the fifth VOC on 26 November 2021. Before the emergence of Omicron variant, three waves of infections and deaths by three distinct variants struck South Africa, with nearly 3 million confirmed cases. The first infections occurred in March 2020, peaked in July and ended in September 2020 (Pulliam et al. 2021 2021) . Additionally, the Omicron variant carries the mutation found in other variants of concern, in which a deletion was found at the peak position 60-79. It has three key mutations similar to the Beta variant and Gamma (P.1) variant, which may increase its ability to escape immunity (Poudel et al. 2022) . In an experiment of incubating virus in convalescent sera from patients infected with prior subtypes, Zhang et al showed early strains or delta infected patients' convalescent sera have relatively low neutralization ability against Omicron virus (Zhang et al. 2021 ). The early strain and Delta-infected patients' neutralizing antibody titer in convalescent sera for Omicron variant decreased 36 times and 39 times. These also contribute to the Omicron variant immune escape ability. Fortunately, the severity of the Omicron variant seems to differ from that of its predecessors. In Tshwane, South Africa, the bed occupancy rate during the peak of the Omicron variant wave was about half of that during the Delta variant wave, suggesting that the number of hospitalization of Omicron variant was lower than that of the Delta variant (Abdullah et al. 2021 ). At the same time, fewer ICU admissions and shorter hospital stays may indicate reduced disease severity caused by Omicron variant. In this work, we develop a model to fit both the reported cases and deaths in South Africa, with the aim to quantify the impact of Omicron variant on the infection fatality rate in South Africa. We obtain reported cases, deaths, excess deaths, and vaccination data from the Our World in We fit our previously proposed Susceptible-Exposed-Infectious-Hospitalized-Death- Africa . We assume that a proportion, denoted as , of the infections was reported and 7%. This can be seen from that the total reported cases in the country is much smaller than estimated proportion of the population being infected based on the seroprevalence from serological studies . We incorporate the vaccination (fully vaccinated, or second dose) data, and fit our model to the reported cases and deaths. We denote the proportion of Omicron variant as . We assume the infection fatality ratio (IFR) of the previous variant is IFR 1 , the IFR of the Omicron variant is IFR 2 . Thus the overall IFR of an one-strain model is (1-) IFR 1 + IFR 2 , i.e, a weighted average of the two IFRs. We assume a vaccine efficacy of 85% against both infection and deaths. Considering the high seroprevalence in South Africa, we assume the eventually 80-85% of the whole population were infected. We note that the re-infection of the Omicron is high, which means Omicron variant has high immune evasion ability. The high relative transmissibility of Omicron variant comes from two sources: namely the enlargement of susceptible pool due to immune evasion and the increase in the intrinsic transmissibility. We consider immunity evasion due to Omicron by allowing a proportion of recovered individuals to move to susceptible on Nov 9, 2021 when Omicron variant was prevalent. We denote the size of the susceptible pool before Omicron evasion as S, then we consider four scenarios: the immune evasion causes the susceptible pool to increase by 0.25*S, 0.5*S, S and 2*S. Our model reads Namely, we assume that the risk of death drops while the vaccination coverage ∫ increases, and we set . We note that the vaccination will only be delivered to those who have not yet be vaccinated. Thus the vaccination rate ̃ takes the form ̃ ∫ , while is the daily vaccination rate per capita. Here we only consider the fully vaccinated (second dose) data and ignore the effects of the first dose since the impact of the first dose would be overtaken by the impact of the second dose. Parameter denotes the risk of hospitalization or severe outcome of infected cases. Since we do not fit hospitalization or severe cases, we cannot estimate , rather we can estimate the product of and which is the IFR when . We previously found that it is convenient to simply assume without changing the fitting performance. We estimate the which is an exponential cubic spline function (Vetterling et al. 1992 ) with 12 nodes spanning over the study period. We fix other parameters which reflects the high efficacy of vaccine against both infection and deaths. We did not explicitly separate cases from natural infection and breakthrough infection. The mean latent period days, days and days are fixed, such that the mean generation time (ie, sum of mean latent period and mean infectious period) equals 5 days (Tang et al. 2021 ) and the mean duration from infection to death is 17 days. We simulate weekly cases and deaths as And we denote the weekly reported cases and deaths as and deaths . We assume Thus we connect the reported cases/deaths and simulated cases/deaths via two Negative Binomial distributions. Thus the log likelihood can be defined (Lin et al. 2018; Zhao et al. 2018) . We fit the model to reported cases and deaths via R package POMP (King, Nguyen, and Ionides 2015; He, Ionides, and King 2010)and report the maximum likelihood estimate for IFR. The 95% confidence interval is defined as the interval of IFR such that the log likelihood of the model given by the data drops by from the maximum log likelihood (He, Ionides, and King 2010) . We found that the COVID-19 case and death reporting in South Africa was consistent overtime. For instance the reported COVID-19 death was consistently 1/3 of the excess deaths. The raw infection fatality rate (IFR) was consistent over time before the emergence of the Omicron variant. After the emergence of Omicron variant, the raw IFR seemingly decreased significantly. In Figure up to as high as 9. We assume that the case testing/reporting effort was consistent, while this might not be always true. When a new variant emerged, the testing effort of cases could be enhanced. Thus our estimated IFR could be underestimated for Omicron variant due to this transient effect. We consider several levels of immunity evasion due to Omicron variant in a single strained model. A multiple strain model could be considered as an alternative approach. We only considered data up to Feb 9, 2022. Our aim is to find an early estimate of IFR for Omicron variant in South Africa. Our estimated IFR is under the situation of an high seroprevalence (infection attack rate) in South Africa, thus it does not reflect the intrinsic IFR of Omicron variant in a largely susceptible population. As a comparison, the raw case fatality rate (CFR) in Hong Kong in the fifth wave of Omicron variant wave is 0.69% by April 4, 2022 ('Latest situation of COVID-19 (as of 4 April 2022)' 2022), while the previous raw CFR is 1.38% (up to February 6, 2022), a reduction of 50%. However, considering the higher under-reporting of cases in the fifth wave than in the previous waves, the reduction could be much higher than 50%. This reduction is partly due to intrinsic feature of Omicron, and partly due to vaccine induced protection. While the raw CFR among unvaccinated in the first wave is 2.05%, which appears to higher than the 1.34% CFR in the previous waves. However, taking into account the high under reporting of cases in the first wave, the CFR of Omicron variant among unvaccinated could be substantially lower than the CFR of previous variants ('Latest situation of COVID-19 (as of 4 April 2022)' 2022). In summary, we found that the relative transmissibility of Omicron variant (including due to immunity escaping) could be more than 3-fold higher than previous variants, which is in line with our previous estimate (Yu et al. 2021 This study only reanalyzed publicly available data which were carried out in accordance with relevant guidelines and regulations. Not applicable. All data are publicly available. https://ourworldindata.org/grapher/covid-variants-area. The Our world in data obtained their variant data from GISAID. The authors declare that they have no competing interests. The work described in this paper was partially supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (HKU C7123-20G). All authors conceived the study, carried out the analysis, wrote the draft, revised the manuscript critically, and approved it for publishing. The work described in this paper was partially supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (HKU C7123-20G). Zhao, Shi, Lewi Stone, Daozhou Gao, and Daihai He. 2018. 'Modelling the large-scale yellow fever outbreak in Luanda, Angola, and the impact of vaccination', PLoS Negl Trop Dis, 12: e0006158. 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