Michelle J. Groome

Bio: Michelle J. Groome is an academic researcher from University of the Witwatersrand. The author has contributed to research in topics: Medicine & Rotavirus vaccine. The author has an hindex of 24, co-authored 60 publications receiving 2813 citations. Previous affiliations of Michelle J. Groome include Medical Research Council & National Research Foundation of South Africa.

Increased risk of SARS-CoV-2 reinfection associated with emergence of Omicron in South Africa

Abstract: We provide two methods for monitoring reinfection trends in routine surveillance data to identify signatures of changes in reinfection risk and apply these approaches to data from South Africa’s severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic to date. Although we found no evidence of increased reinfection risk associated with circulation of the Beta (B.1.351) or Delta (B.1.617.2) variants, we did find clear, population-level evidence to suggest immune evasion by the Omicron (B.1.1.529) variant in previously infected individuals in South Africa. Reinfections occurring between 1 November 2021 and 31 January 2022 were detected in individuals infected in all three previous waves, and there has been an increase in the risk of having a third infection since mid-November 2021. Description Omicron and reinfection risk So far, our experience with pandemic coronavirus has been that the emergence of new variants is not detected until there has been substantial community transmission. Early in November 2021, South African scientists spotted reinfections consistent with the timing of the emergence of the Omicron variant (B.1.1.529). In a population largely untouched by vaccines but widely infected, Pulliam et al. found that Beta or Delta variants rarely caused reinfection. However, after 31 October 2021, individuals were found who had had three experiences of infection (see the Perspective by Zelner and Eisenberg). The culprit was the rapidly emerging Omicron variant, with multiple mutations in the Spike protein. This variant’s chief advantage is its ability to evade naturally acquired immunity. Fortunately, the Omicron variant does not fully evade vaccine-derived immunity, but only those privileged to have been vaccinated can benefit. —CA Analysis of routine surveillance data from South Africa indicates that the Omicron variant of SARS-CoV-2 evades immunity from prior infection. INTRODUCTION Globally, there have been more than 404 million cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with 5.8 million confirmed deaths as of February 2022. South Africa has experienced four waves of SARS-CoV-2 transmission, with the second, third, and fourth waves being driven by the Beta, Delta, and Omicron variants, respectively. A key question with the emergence of new variants is the extent to which they are able to reinfect those who have had a prior natural infection. RATIONALE We developed two approaches to monitor routine epidemiological surveillance data to determine whether SARS-CoV-2 reinfection risk has changed through time in South Africa in the context of the emergence of the Beta (B.1.351), Delta (B.1.617.2), and Omicron (B.1.1.529) variants. We analyzed line-list data on positive tests for SARS-CoV-2 with specimen receipt dates between 4 March 2020 and 31 January 2022 collected through South Africa’s National Notifiable Medical Conditions Surveillance System. Individuals having sequential positive tests at least 90 days apart were considered to have suspected reinfections. Our routine monitoring of reinfection risk included comparison of reinfection rates with the expectation under a null model (approach 1) and estimation of the time-varying hazards of infection and reinfection throughout the epidemic (approach 2) based on model-based reconstruction of the susceptible populations eligible for primary and second infections. RESULTS A total of 105,323 suspected reinfections were identified among 2,942,248 individuals with laboratory-confirmed SARS-CoV-2 who had a positive test result at least 90 days before 31 January 2022. The number of reinfections observed through the end of the third wave in September 2021 was consistent with the null model of no change in reinfection risk (approach 1). Although increases in the hazard of primary infection were observed after the introduction of both the Beta and Delta variants, no corresponding increase was observed in the reinfection hazard (approach 2). Contrary to expectation, the estimated hazard ratio for reinfection versus primary infection was lower during waves driven by the Beta and Delta variants than for the first wave: the relative hazard ratio for wave 2 versus wave 1 was 0.71 [95% confidence interval (95% CI): 0.60 to 0.85]; the relative hazard ratio for wave 3 versus wave 1 was 0.54 (95% CI: 0.45 to 0.64). By contrast, the recent spread of the Omicron variant has been associated with an increase in reinfection hazard coefficient. The estimated relative hazard ratio for reinfection versus primary infection versus wave 1 was 1.75 (95% CI: 1.48 to 2.10) for the period of Omicron emergence (1 November 2021 to 30 November 2021) and 1.70 (95% CI: 1.44 to 2.04) for wave 4 versus wave 1. Individuals with identified reinfections since 1 November 2021 had experienced primary infections in all three prior waves, and an increase in third infections has been detected since mid-November 2021. Many individuals experiencing third infections had second infections during the third (Delta) wave that ended in September 2021, strongly suggesting that these infections resulted from immune evasion rather than waning immunity. CONCLUSION Population-level evidence suggests that the Omicron variant is associated with a marked ability to evade immunity from prior infection. In contrast, there is no population-wide epidemiological evidence of immune escape associated with the Beta or Delta variants. This finding has important implications for public health planning, particularly in countries such as South Africa with high rates of immunity from prior infection. The further development of methods to track reinfection risk during pathogen emergence, including refinements to assess the impact of waning immunity, account for vaccine-derived protection, and monitor the risk of multiple reinfections, will be important for future pandemic preparedness. SARS-CoV-2 reinfection patterns in South Africa. South Africa has experienced four waves of SARS-CoV-2 transmission, each driven by the emergence of a new variant. Reinfection of previously infected individuals was relatively rare through the end of the third wave. Methods developed in South Africa to monitor reinfection trends led to the early detection of increased reinfection risk associated with the Omicron variant.

Age-dependent effects in the transmission and control of COVID-19 epidemics.

Abstract: The COVID-19 pandemic has shown a markedly low proportion of cases among children1–4. Age disparities in observed cases could be explained by children having lower susceptibility to infection, lower propensity to show clinical symptoms or both. We evaluate these possibilities by fitting an age-structured mathematical model to epidemic data from China, Italy, Japan, Singapore, Canada and South Korea. We estimate that susceptibility to infection in individuals under 20 years of age is approximately half that of adults aged over 20 years, and that clinical symptoms manifest in 21% (95% credible interval: 12–31%) of infections in 10- to 19-year-olds, rising to 69% (57–82%) of infections in people aged over 70 years. Accordingly, we find that interventions aimed at children might have a relatively small impact on reducing SARS-CoV-2 transmission, particularly if the transmissibility of subclinical infections is low. Our age-specific clinical fraction and susceptibility estimates have implications for the expected global burden of COVID-19, as a result of demographic differences across settings. In countries with younger population structures—such as many low-income countries—the expected per capita incidence of clinical cases would be lower than in countries with older population structures, although it is likely that comorbidities in low-income countries will also influence disease severity. Without effective control measures, regions with relatively older populations could see disproportionally more cases of COVID-19, particularly in the later stages of an unmitigated epidemic. A new epidemiological study shows reduced susceptibility to SARS-CoV-2 and decreased risk of developing severe symptoms in people aged younger than 20 years, suggesting that children have limited contribution to spread of COVID-19.

Estimates of the global, regional, and national morbidity, mortality, and aetiologies of lower respiratory infections in 195 countries, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016

Abstract: Summary Background Lower respiratory infections are a leading cause of morbidity and mortality around the world The Global Burden of Diseases, Injuries, and Risk Factors (GBD) Study 2016, provides an up-to-date analysis of the burden of lower respiratory infections in 195 countries This study assesses cases, deaths, and aetiologies spanning the past 26 years and shows how the burden of lower respiratory infection has changed in people of all ages Methods We used three separate modelling strategies for lower respiratory infections in GBD 2016: a Bayesian hierarchical ensemble modelling platform (Cause of Death Ensemble model), which uses vital registration, verbal autopsy data, and surveillance system data to predict mortality due to lower respiratory infections; a compartmental meta-regression tool (DisMod-MR), which uses scientific literature, population representative surveys, and health-care data to predict incidence, prevalence, and mortality; and modelling of counterfactual estimates of the population attributable fraction of lower respiratory infection episodes due to Streptococcus pneumoniae, Haemophilus influenzae type b, influenza, and respiratory syncytial virus We calculated each modelled estimate for each age, sex, year, and location We modelled the exposure level in a population for a given risk factor using DisMod-MR and a spatio-temporal Gaussian process regression, and assessed the effectiveness of targeted interventions for each risk factor in children younger than 5 years We also did a decomposition analysis of the change in LRI deaths from 2000–16 using the risk factors associated with LRI in GBD 2016 Findings In 2016, lower respiratory infections caused 652 572 deaths (95% uncertainty interval [UI] 586 475–720 612) in children younger than 5 years (under-5s), 1 080 958 deaths (943 749–1 170 638) in adults older than 70 years, and 2 377 697 deaths (2 145 584–2 512 809) in people of all ages, worldwide Streptococcus pneumoniae was the leading cause of lower respiratory infection morbidity and mortality globally, contributing to more deaths than all other aetiologies combined in 2016 (1 189 937 deaths, 95% UI 690 445–1 770 660) Childhood wasting remains the leading risk factor for lower respiratory infection mortality among children younger than 5 years, responsible for 61·4% of lower respiratory infection deaths in 2016 (95% UI 45·7–69·6) Interventions to improve wasting, household air pollution, ambient particulate matter pollution, and expanded antibiotic use could avert one under-5 death due to lower respiratory infection for every 4000 children treated in the countries with the highest lower respiratory infection burden Interpretation Our findings show substantial progress in the reduction of lower respiratory infection burden, but this progress has not been equal across locations, has been driven by decreases in several primary risk factors, and might require more effort among elderly adults By highlighting regions and populations with the highest burden, and the risk factors that could have the greatest effect, funders, policy makers, and programme implementers can more effectively reduce lower respiratory infections among the world's most susceptible populations Funding Bill & Melinda Gates Foundation

Age-dependent effects in the transmission and control of COVID-19 epidemics

Abstract: The COVID-19 pandemic has shown a markedly low proportion of cases among children. Age disparities in observed cases could be explained by assortative mixing patterns and reactive school closures which decrease mixing between children, or by children exhibiting lower susceptibility to infection, or by children having a lower propensity to show clinical symptoms. We formally test these hypotheses by fitting an age-structured mathematical model to epidemic data from six countries, finding strong age dependence in the probability of developing clinical symptoms, rising from around 20% in under 10s to over 70% in older adults. We find that interventions aimed at halting transmission in children may have minimal effects on preventing cases depending on the relative transmissibility of subclinical infections. Our estimated age-specific clinical fraction has implications for the expected global burden of clinical cases because of demographic differences across settings. In younger populations, the expected clinical attack rate would be lower, although it is likely that comorbidities in low-income countries will affect disease severity. Without effective control measures, regions with older populations may see disproportionally more clinical cases, particularly in the later stages of the pandemic.