Literature DB >> 35794899

Outcomes of laboratory-confirmed SARS-CoV-2 infection during resurgence driven by Omicron lineages BA.4 and BA.5 compared with previous waves in the Western Cape Province, South Africa.

Mary-Ann Davies1,2,3, Erna Morden1,3, Petro Rosseau4, Juanita Arendse5, Jamy-Lee Bam1, Linda Boloko6,7, Keith Cloete5, Cheryl Cohen8,9, Nicole Chetty1,2, Pierre Dane1,2, Alexa Heekes1,2, Nei-Yuan Hsiao10,11, Mehreen Hunter1,3, Hannah Hussey1,3,12, Theuns Jacobs1, Waasila Jassat8, Saadiq Kariem5, Reshma Kassanjee2, Inneke Laenen1,13, Sue Le Roux5,14, Richard Lessells15, Hassan Mahomed12,13, Deborah Maughan6,16, Graeme Meintjes6,16, Marc Mendelson6,7, Ayanda Mnguni17, Melvin Moodley1, Katy Murie5,12, Jonathan Naude18, Ntobeko A B Ntusi6,16,19, Masudah Paleker1,13, Arifa Parker20,21, David Pienaar22, Wolfgang Preiser11,23, Hans Prozesky20,21, Peter Raubenheimer6,16, Liezel Rossouw5, Neshaad Schrueder20,24, Barry Smith5,14, Mariette Smith1,2, Wesley Solomon4, Greg Symons6,16, Jantjie Taljaard20,21, Sean Wasserman6,7, Robert J Wilkinson25,26,27, Milani Wolmarans4, Nicole Wolter8,28, Andrew Boulle1,2,3.   

Abstract

Objective: We aimed to compare clinical severity of Omicron BA.4/BA.5 infection with BA.1 and earlier variant infections among laboratory-confirmed SARS-CoV-2 cases in the Western Cape, South Africa, using timing of infection to infer the lineage/variant causing infection.
Methods: We included public sector patients aged ≥20 years with laboratory-confirmed COVID-19 between 1-21 May 2022 (BA.4/BA.5 wave) and equivalent prior wave periods. We compared the risk between waves of (i) death and (ii) severe hospitalization/death (all within 21 days of diagnosis) using Cox regression adjusted for demographics, comorbidities, admission pressure, vaccination and prior infection.
Results: Among 3,793 patients from the BA.4/BA.5 wave and 190,836 patients from previous waves the risk of severe hospitalization/death was similar in the BA.4/BA.5 and BA.1 waves (adjusted hazard ratio [aHR] 1.12; 95% confidence interval [CI] 0.93; 1.34). Both Omicron waves had lower risk of severe outcomes than previous waves. Prior infection (aHR 0.29, 95% CI 0.24; 0.36) and vaccination (aHR 0.17; 95% CI 0.07; 0.40 for boosted vs. no vaccine) were protective.
Conclusion: Disease severity was similar amongst diagnosed COVID-19 cases in the BA.4/BA.5 and BA.1 periods in the context of growing immunity against SARS-CoV-2 due to prior infection and vaccination, both of which were strongly protective.

Entities:  

Year:  2022        PMID: 35794899      PMCID: PMC9258293          DOI: 10.1101/2022.06.28.22276983

Source DB:  PubMed          Journal:  medRxiv


Background

The Omicron SARS-CoV-2 variant of concern (VOC) has been dominant globally since November 2021, with several lineages causing surges in infections (Iketani et al., 2022, Tegally et al., 2022, Viana et al., 2022). South Africa experienced an initial large BA.1 infection surge from November 2021 to January 2022. BA.1 was then replaced by BA.2 but with no increase in cases numbers, and this was followed by a BA.4/BA.5 infection surge between April and June 2022 (Tegally et al., 2022, Viana et al., 2022). The combination of mutations in BA.4/BA.5 appear to confer a growth advantage over BA.2, as well as immune escape from vaccine-derived and BA.1 elicited antibodies (Khan et al., 2022, Tegally et al., 2022). A growing number of BA.4 and BA.5 infections are now being reported globally (Callaway, 2022, UK Health Security Agency, 2022). We therefore compared outcomes of laboratory-confirmed SARS-CoV-2 infections during the recent resurgence (proxy for BA.4/ BA.5 infection) with outcomes during each of the four previous waves in South Africa, each of which were caused by a different variant or lineage, using data on patients with laboratory-confirmed SARS-CoV-2 infection aged ≥20 years using public sector services in the Western Cape Province, South Africa.

Methods:

We conducted a cohort study using de-identified data from the Western Cape Provincial Health Data Centre (WCPHDC) of public sector patients aged ≥20 years with a laboratory confirmed COVID-19 diagnosis (positive SARS-CoV-2 PCR or antigen test). The Western Cape has nearly 7 million inhabitants, of whom approximately 75% use public sector health services (Western Cape Department of Health, 2020). The WCPHDC and methods for this study have previously been described in detail (Boulle et al., 2019, Davies et al., 2022, Hussey et al., 2022, Western Cape Department of Health in collaboration with the National Institute for Communicable Diseases, 2020). Briefly, for this analysis, waves of infection were defined as starting and ending when the 7-day moving average of public sector COVID-19 hospital admissions exceeded and dropped below 5 and 12 per million population respectively. We included cases diagnosed from seven days before the wave start to seven days before the wave end date to account for the lag between infection/first symptoms and hospitalization. We thus included data on cases diagnosed from 1–21 May 2022 for the BA.4/BA.5 wave, with follow-up through to 11 June 2022, corresponding to the period when BA.4/BA.5 dominated in the province (Network for Genomic Surveillance in South Africa, 2022). We used Cox regression adjusted for age, sex, geographic location, comorbidities, service pressure (number of weekly admissions in the health district) at time of diagnosis, prior diagnosed infection (≥1 laboratory confirmed SARS-CoV-2 diagnosis ≥90 days previously) and SARS-CoV-2 vaccination to assess differences in the following COVID-19 outcomes between waves driven by different variants: (i) death and (ii) death or severe hospitalization (admission to intensive care or mechanical ventilation or oral/intravenous steroid prescription). We only included outcomes within 21 days of COVID-19 diagnosis for comparable ascertainment across all waves. All deaths within 21 days of a COVID-19 diagnosis were included unless a clear non-COVID-19 cause of death was recorded. For patients with recorded South African national identity numbers, data are linked to the South African vital registry to identify deaths not recorded in the WCPHDC. Vaccination data was obtained by linking the South African national identifier to the Electronic Vaccine Data System which records all vaccines administered in the country. The only vaccines available in South Africa to date are BNT162b and Ad26.COV2.S. Vaccination status was categorized as either (i) “boosted” (three or more homologous or heterologous doses of any vaccine), (ii) “two doses” (two doses of any vaccine) or (iii) “single dose” (single dose of Ad26.COV2.S), as the latter is considered complete primary vaccination in South Africa. The study was approved by the University of Cape Town and Stellenbosch University Health Research Ethics Committees and Western Cape Government: Health. Individual informed consent requirement was waived for this secondary analysis of de-identified data.

Results

We included 3,793 patients diagnosed in the BA.4/BA.5 wave and 27,614 (BA.1), 68,715 (Delta), 54,268 (Beta) and 40,204 (ancestral) in waves driven by previous variants (Table 1). The proportion with prior diagnosed infection was substantially higher in the BA.4/BA.5 (18.9%) and BA.1 (11.9%) waves compared to previous waves (<3%). In the BA.4/BA.5 wave, 12.9% of COVID-19 cases had received “single dose” Ad26.COV2.S vaccination, 36.1% had received “two doses” and 6.7% were “boosted” vaccinees.
Table 1:

Characteristics and outcomes of COVID-19 cases included from each infection period in the Western Cape

Ancestral wave 25 Apr to 22 Jul 2020[a] (n=40,204)Beta wave 3 Nov 2020 to 22 Jan 2021[a] (n=54,268)Delta wave 30 May to 10 Sep 2021[a] (n=68,750)BA.1 wave 27 Nov 2021 to 12 Jan 2022[a] (n=27,614)BA.4/BA.5 wave 1 May to 21 May 2022[a] (n=3,793)
Male sex 13,380 (33.3%)19,083 (35.2%)25,948 (37.7%)9,630 (34.9%)1,327 (35.0%)
Age
20–39 years18,720 (46.6%)21,839 (40.2%)29,720 (43.2%)13,944 (50.5%)1,783 (47.0%)
40–49 years8,280 (20.6%)10,594 (19.5%)14,163 (20.6%)4,905 (17.8%)767 (20.2%)
50–59 years6,982 (17.4%)10,493 (19.3%)13,294 (19.3%)4,216 (15.3%)623 (16.4%)
60–69 years3,733 (9.3%)6,929 (12.8%)6,780 (9.9%)2,554 (9.3%)333 (8.8%)
≥70 years2,489 (6.2%)4,413 (8.1%)4,793 (7.0%)1,995 (7.2%)287 (7.6%)
Non-communicable diseases
diabetes8,265 (20.6%)11,509 (21.1%)11,581 (16.9%)3,627 (13.1%)406 (10.7%)
hypertension13,065 (32.5%)19,070 (35.1%)21,170 (30.8%)7,063 (25.6%)842 (22.2%)
chronic kidney disease2,013 (5.0%)2,778 (5.2%)3,018 (4.4%)958 (3.5%)124 (3.3%)
chronic pulmonary disease / asthma3,099 (7.7%)4,661 (8.6%)6,434 (9.4%)3,040 (11.0%)411 (10.8%)
Tuberculosis
previous tuberculosis2,777 (6.9%)3,450 (6.4%)4,850 (7.1%)2,229 (8.1%)232 (6.1%)
current tuberculosis513 (1.3%)555 (1.0%)803 (1.2%)578 (2.1%)76 (2.0%)
HIV positive 6,203 (15.4%)5,512 (10.2%)5,925 (8.6%)3,298 (11.9%)307 (8.1%)
Prior diagnosed SARS-CoV-2 infection 0 (0%)618 (1.1%)1,798 (2.6%)3,179 (11.5%)715 (18.9%)
Vaccination b
noneN/AN/A63,644 (92.6%)14,471 (52.4%)1,535 (40.5%)
single dose Ad26.COV2.SN/AN/A2,501 (3.6%)4,069 (14.7%)488 (12.9%)
single dose BNT162b2N/AN/A2,289 (3.3%)1,144 (4.1%)147 (3.9%)
2 doses Ad26.COV2.SN/AN/A30 (0.04%)1,127 (4.1%)298 (7.9%)
2 doses BNT162b2N/AN/A286 (0.4%)6,763 (24.5%)1,067 (28.1%)
2 doses Ad26.COV2.S + BNT162b2N/AN/AN/AN/A5 (0.1%)
≥3 doses Ad26.COV2.SN/AN/AN/A36 (0.1%)38 (1.0%)
≥3 doses BNT162b2N/AN/AN/A4 (0.01%)192 (5.1%)
≥3 doses Ad26.COV2.S + BNT162b2N/AN/AN/AN/A23 (0.6%)
Outcomes within 21 days of diagnosis
severe admission (not deceased)[c]N/A[c]1,916 (3.5%)2,066 (3.0%)481 (1.7%)61 (1.6%)
death2,147 (5.3%)3,717 (6.9%)4368 (6.4%)699 (2.5%)70 (1.9%)

Date of diagnoses for cases included in each wave. We included cases diagnosed from 7 days prior to the “wave start” to the date of wave end (deemed to occur when 7 day moving average of daily new public sector admissions exceeded 5/million (start) and dropped below 12/million (end) respectively).

Vaccination is summarized as vaccine type and number of doses provided diagnosis was ≥28 days after first dose, ≥14 days after second dose, and ≥7 days after third dose;

Admission to an intensive care unit, mechanical ventilation or prescription of oral or intravenous steroids; not reported for wave 1 as steroids not widely used until after 16 June 2020. N/A = not applicable

The adjusted hazard of severe hospitalization or death in the BA.4/BA.5 wave was similar to the BA.1 wave (adjusted hazard ratio [aHR] 1.12; 95% confidence interval [CI]: 0.93; 1.34) (Table 2). Both Omicron-driven waves had lower hazards of severe hospitalization or death than previous waves (Table 2). Prior diagnosed infection was strongly protective against severe hospitalization or death (aHR 0.29; 95% CI 0.24; 0.36) as was vaccination with aHR (95% CI) of 0.17 (0.07; 0.40); 0.37 (0.33; 0.42) and 0.26 (0.21; 0.32) for “boosted”, “two doses” and “single dose”, respectively. In a model not adjusting for vaccination and prior diagnosed infection, the hazard of severe hospitalization or death in the BA.4/BA.5 vs. BA.1 waves was reduced compared to the fully adjusted model (aHR 0.90; 95% CI: 0.75; 1.08). In an analysis restricted to the BA.4/BA.5 period, prior diagnosed infection remained strongly protective against severe hospitalization or death (aHR 0.23; 95% CI 0.10; 0.52) as did vaccination (aHR [95% CI]: 0.20 (0.08; 0.49); 0.39 (0.25; 0.59) and 0.51 (0.27; 0.99) for “boosted”, “two doses” and “single dose”, respectively. Results were all similar when examining the outcome of death alone.
Table 2:

Associations between different infection periods and severe COVID-19 outcomes adjusted for patient characteristics, sub-district, vaccination, and prior diagnosed infection using Cox regression.

Outcome = death not adjusted for vaccination and prior infectionOutcome = death adjusted for vaccination and prior infectionOutcome = severe hospitalization[a]/death not adjusted for vaccination or prior diagnosed infectionOutcome = severe hospitalization[a]/death adjusted for vaccination or prior diagnosed infection
Adjusted[b] HR95% CIAdjusted HR95% CIAdjusted[b] HR95% CIAdjusted HR95% CI
Male sex (vs. female) 1.401.34; 1.451.401.34; 1.451.271.23; 1.311.261.22; 1.30
Age (vs. 20–39 years)
40–49 years2.542.30; 2.812.572.33; 2.842.001.87; 2.152.041.90; 2.19
50–59 years5.464.99; 5.975.565.08; 6.083.423.21; 3.653.503.28; 3.74
60–69 years12.5511.47; 13.7312.8811.77; 14.106.395.97; 6.836.566.13; 7.01
≥70 years23.1921.15; 25.4323.9321.82; 26.2410.359.65; 11.0910.659.94; 11.42
Comorbidities (vs. comorbidity absent)
diabetes2.011.92; 2.102.011.93; 2.101.971.89; 2.041.981.91; 2.06
hypertension1.081.03; 1.131.071.02; 1.121.181.14; 1.231.171.13; 1.22
chronic kidney disease1.901.80; 2.001.901.81; 2.001.631.56; 1.701.631.56; 1.70
chronic pulmonary disease / asthma0.980.93; 1.040.990.93; 1.041.181.13; 1.231.191.14; 1.24
previous tuberculosis1.301.20; 1.401.281.19; 1.381.251.17; 1.331.231.16; 1.31
current tuberculosis2.532.20; 2.912.442.13; 2.812.892.59; 3.232.792.50; 3.11
HIV1.601.48; 1.721.601.49; 1.721.541.45; 1.641.541.45; 1.64
Number of admissions in district in week of diagnosis (vs <1/3 of maximum)
1/3 to <2/31.111.05; 1.171.121.06; 1.181.030.98; 1.081.040.99; 1.09
≥2/31.121.05; 1.201.131.06; 1.211.050.99; 1.111.061.00; 1.12
Prior diagnosed SARS CoV-2 infection
Yes (vs none)0.510.42; 0.630.290.24; 0.36
Vaccination (vs. None) c
single dose Ad26.COV2.S0.240.18; 0.330.260.21; 0.32
two doses (Ad26.COV2.S and/or BNT162b2)0.360.31; 0.420.370.33; 0.42
boosted (≥ 3doses Ad26.COV2.S and/or BNT162b2)0.060.01; 0.400.170.07; 0.40
Wave period (dominant variant)
wave 1 (ancestral)2.081.90; 2.281.301.17; 1.44N/A[a]N/A[a]
wave 2 (Beta)2.352.16; 2.571.471.34; 1.622.061.93; 2.201.281.20; 1.38
wave 3 (Delta)2.582.37; 2.811.751.59; 1.922.162.03; 2.291.441.35; 1.54
wave 4 (Omicron BA.1)RefRefRefRef
wave 5 (Omicron BA.4/BA.5)0.930.72; 1.201.160.90; 1.500.900.75; 1.081.120.93; 1.34

Admission to an intensive care unit, mechanical ventilation or prescription of oral or intravenous steroids; not reported for wave 1 as steroids not widely used until after 16 June 2020.

Adjusted for all variables shown in the table as well as subdistrict/district, but not for vaccination or prior diagnosed infection

Vaccination status is categorized as “single dose” (≥28 days after single dose Ad26.COV2.S), “two doses” (≥14 days after second dose of homologous or heterologous vaccination with Ad26.COV2.S and/or BNT162b2), and “boosted” (≥7 days after third dose of homologous or heterologous vaccination with Ad26.COV2.S and/or BNT162b2); HR = hazard ratio; CI = confidence interval; N/A = not applicable

Discussion

Using the period of diagnosis as a proxy for being infected with different Omicron lineages in the Western Cape, we found no difference in the risk of severe COVID-19 hospitalization or death during the BA.4/BA.5 period compared to the BA.1 period, both of which had better outcomes than previous waves. Strong protection against severe COVID-19 conferred by prior infection and vaccination was retained in the BA.4/BA.5 wave, with three homologous doses of Ad26.COV2.S or BNT162b2 or a heterologous combination of these providing 83% protection (95% CI 60; 93%) against severe COVID-19 hospitalization or death amongst laboratory-confirmed cases. A study in animals recently suggested that BA.4/BA.5 may be more pathogenic than BA.2 (Kimura et al., 2022). Although we did not compare BA.4/BA.5 with BA.2 directly as BA.2 did not cause a distinguishable surge in infections in the Western Cape, disease severity of BA.2 and BA.1 are similar (Lewnard et al., 2022) and we found no evidence of worse clinical outcomes with BA.4/BA.5 compared to BA.1. Nonetheless, our findings need to be interpreted in the context of South African SARS-CoV-2 epidemiology with progressively increasing seroprevalence due to prior infection (mostly undiagnosed) and/or vaccination (Bingham et al., 2022, Madhi et al., 2022, Sun et al., 2022). For example, among blood donors, after the BA.1 wave the estimated national prevalence of anti-nucleocapsid antibodies was 87% (indicating previous infection) with a further 10% having anti-spike antibodies only (suggesting vaccination without prior infection) (Bingham et al., 2022). Indeed, our finding that the aHR shifted towards a lower risk of severe outcomes during BA.4/BA.5 vs. BA.1 in models not accounting for vaccination and prior diagnosed infection, suggests that the observed continued ecologic decoupling of COVID-19 cases and severe outcomes is at least partly due to growing protection against severe disease from both prior infection and vaccination. With the progression of the SARS-CoV-2 pandemic globally, it is increasingly difficult to determine the clinical severity of any variant in a completely naïve individual. However, for health service planning this is less relevant than the real-world effect in populations with varying degrees of immune protection (Mefsin et al., 2022). For example, although we showed similar risk of severe hospitalization or death in the BA.4/BA.5 and BA.1 waves when adjusted for vaccination and prior diagnosed infection, the actual burden of admissions and deaths was much lower in the BA.4/BA.5 waves, with the peak 7-day moving average of admissions and deaths being 222 and 36 in the BA.1 wave vs. 66 and 9 in the BA.4/BA.5 wave. The ability to use routine data to rapidly assess the relative severity of waves caused by different lineages and variants adjusted for comorbidities, vaccination and prior infection has been especially valuable for local health service planning (Davies et al., 2022). To our knowledge, this is one of the first comparisons of clinical severity of BA.4/BA.5 infections with previous variants with relatively complete adjustment for comorbidities and vaccination among all diagnosed cases. Nonetheless, this type of data and analysis have several limitations which have been described in detail previously (Davies et al., 2022). These include using the time of infection as a proxy for the variant causing infection rather than actual genomic sequencing or PCR test proxies (Wolter et al., 2022) which would be more accurate and overcome challenges with comparing disease severity across waves due to differences in testing practices, treatment availability and health service pressures. Notably, testing in the BA.4/BA.5 wave was at the lowest levels since the start of the pandemic with less testing of patients with milder disease, hence we may have over-estimated disease severity in this wave. With routine data we were unable to distinguish between severe hospitalizations and deaths where the diagnosis of COVID-19 may have been incidental or contributory rather than causal, and had incomplete ascertainment of key covariates especially prior diagnosed infection due to substantial missed diagnoses, vaccinations received outside of the province or without submitting a South African identity number and undiagnosed comorbidities as we can only adjust for those algorithmically identified in the WCPHDC. In conclusion, we found similar disease severity amongst diagnosed COVID-19 cases in the BA.4/BA.5 and BA.1 periods in the context of growing immunity against SARS-CoV-2 with strong protection against severe outcomes conferred by prior infection and vaccination, especially if boosted. Ensuring that individuals at high risk of severe COVID-19 outcomes have at least three vaccine doses remains a key strategy for limiting the public health impact of further COVID-19 waves.
  12 in total

1.  What Omicron's BA.4 and BA.5 variants mean for the pandemic.

Authors:  Ewen Callaway
Journal:  Nature       Date:  2022-06       Impact factor: 49.962

2.  Clinical outcomes associated with SARS-CoV-2 Omicron (B.1.1.529) variant and BA.1/BA.1.1 or BA.2 subvariant infection in Southern California.

Authors:  Joseph A Lewnard; Vennis X Hong; Manish M Patel; Rebecca Kahn; Marc Lipsitch; Sara Y Tartof
Journal:  Nat Med       Date:  2022-06-08       Impact factor: 87.241

3.  Risk Factors for Coronavirus Disease 2019 (COVID-19) Death in a Population Cohort Study from the Western Cape Province, South Africa.

Authors: 
Journal:  Clin Infect Dis       Date:  2021-10-05       Impact factor: 9.079

4.  SARS-CoV-2 transmission, persistence of immunity, and estimates of Omicron's impact in South African population cohorts.

Authors:  Kaiyuan Sun; Stefano Tempia; Jackie Kleynhans; Anne von Gottberg; Meredith L McMorrow; Nicole Wolter; Jinal N Bhiman; Jocelyn Moyes; Mignon du Plessis; Maimuna Carrim; Amelia Buys; Neil A Martinson; Kathleen Kahn; Stephen Tollman; Limakatso Lebina; Floidy Wafawanaka; Jacques D du Toit; Francesc Xavier Gómez-Olivé; Thulisa Mkhencele; Cécile Viboud; Cheryl Cohen
Journal:  Sci Transl Med       Date:  2022-08-24       Impact factor: 19.319

5.  Assessing the clinical severity of the Omicron variant in the Western Cape Province, South Africa, using the diagnostic PCR proxy marker of RdRp target delay to distinguish between Omicron and Delta infections - a survival analysis.

Authors:  Hannah Hussey; Mary-Ann Davies; Alexa Heekes; Carolyn Williamson; Ziyaad Valley-Omar; Diana Hardie; Stephen Korsman; Deelan Doolabh; Wolfgang Preiser; Tongai Maponga; Arash Iranzadeh; Sean Wasserman; Linda Boloko; Greg Symons; Peter Raubenheimer; Arifa Parker; Neshaad Schrueder; Wesley Solomon; Petro Rousseau; Nicole Wolter; Waasila Jassat; Cheryl Cohen; Richard Lessells; Robert J Wilkinson; Andrew Boulle; Nei-Yuan Hsiao
Journal:  Int J Infect Dis       Date:  2022-02-27       Impact factor: 12.074

6.  Population Immunity and Covid-19 Severity with Omicron Variant in South Africa.

Authors:  Shabir A Madhi; Gaurav Kwatra; Jonathan E Myers; Waasila Jassat; Nisha Dhar; Christian K Mukendi; Amit J Nana; Lucille Blumberg; Richard Welch; Nicoletta Ngorima-Mabhena; Portia C Mutevedzi
Journal:  N Engl J Med       Date:  2022-02-23       Impact factor: 91.245

7.  Antibody evasion properties of SARS-CoV-2 Omicron sublineages.

Authors:  Sho Iketani; Lihong Liu; Yicheng Guo; Liyuan Liu; Jasper F-W Chan; Yiming Huang; Maple Wang; Yang Luo; Jian Yu; Hin Chu; Kenn K-H Chik; Terrence T-T Yuen; Michael T Yin; Magdalena E Sobieszczyk; Yaoxing Huang; Kwok-Yung Yuen; Harris H Wang; Zizhang Sheng; David D Ho
Journal:  Nature       Date:  2022-03-03       Impact factor: 69.504

8.  Outcomes of laboratory-confirmed SARS-CoV-2 infection in the Omicron-driven fourth wave compared with previous waves in the Western Cape Province, South Africa.

Authors:  Mary-Ann Davies; Reshma Kassanjee; Petro Rousseau; Erna Morden; Leigh Johnson; Wesley Solomon; Nei-Yuan Hsiao; Hannah Hussey; Graeme Meintjes; Masudah Paleker; Theuns Jacobs; Peter Raubenheimer; Alexa Heekes; Pierre Dane; Jamy-Lee Bam; Mariette Smith; Wolfgang Preiser; David Pienaar; Marc Mendelson; Jonathan Naude; Neshaad Schrueder; Ayanda Mnguni; Sue Le Roux; Kathleen Murie; Hans Prozesky; Hassan Mahomed; Liezel Rossouw; Sean Wasserman; Deborah Maughan; Linda Boloko; Barry Smith; Jantjie Taljaard; Greg Symons; Ntobeko A B Ntusi; Arifa Parker; Nicole Wolter; Waasila Jassat; Cheryl Cohen; Richard Lessells; Robert J Wilkinson; Juanita Arendse; Saadiq Kariem; Melvin Moodley; Milani Wolmarans; Keith Cloete; Andrew Boulle
Journal:  Trop Med Int Health       Date:  2022-05-10       Impact factor: 3.918

9.  Data Centre Profile: The Provincial Health Data Centre of the Western Cape Province, South Africa.

Authors:  A Boulle; A Heekes; N Tiffin; M Smith; T Mutemaringa; N Zinyakatira; F Phelanyane; C Pienaar; K Buddiga; E Coetzee; R van Rooyen; R Dyers; N Fredericks; A Loff; L Shand; M Moodley; I de Vega; K Vallabhjee
Journal:  Int J Popul Data Sci       Date:  2019-11-20

10.  Rapid epidemic expansion of the SARS-CoV-2 Omicron variant in southern Africa.

Authors:  Raquel Viana; Sikhulile Moyo; Daniel G Amoako; Houriiyah Tegally; Cathrine Scheepers; Christian L Althaus; Ugochukwu J Anyaneji; Phillip A Bester; Maciej F Boni; Mohammed Chand; Wonderful T Choga; Rachel Colquhoun; Michaela Davids; Koen Deforche; Deelan Doolabh; Louis du Plessis; Susan Engelbrecht; Josie Everatt; Jennifer Giandhari; Marta Giovanetti; Diana Hardie; Verity Hill; Nei-Yuan Hsiao; Arash Iranzadeh; Arshad Ismail; Charity Joseph; Rageema Joseph; Legodile Koopile; Sergei L Kosakovsky Pond; Moritz U G Kraemer; Lesego Kuate-Lere; Oluwakemi Laguda-Akingba; Onalethatha Lesetedi-Mafoko; Richard J Lessells; Shahin Lockman; Alexander G Lucaci; Arisha Maharaj; Boitshoko Mahlangu; Tongai Maponga; Kamela Mahlakwane; Zinhle Makatini; Gert Marais; Dorcas Maruapula; Kereng Masupu; Mogomotsi Matshaba; Simnikiwe Mayaphi; Nokuzola Mbhele; Mpaphi B Mbulawa; Adriano Mendes; Koleka Mlisana; Anele Mnguni; Thabo Mohale; Monika Moir; Kgomotso Moruisi; Mosepele Mosepele; Gerald Motsatsi; Modisa S Motswaledi; Thongbotho Mphoyakgosi; Nokukhanya Msomi; Peter N Mwangi; Yeshnee Naidoo; Noxolo Ntuli; Martin Nyaga; Lucier Olubayo; Sureshnee Pillay; Botshelo Radibe; Yajna Ramphal; Upasana Ramphal; James E San; Lesley Scott; Roger Shapiro; Lavanya Singh; Pamela Smith-Lawrence; Wendy Stevens; Amy Strydom; Kathleen Subramoney; Naume Tebeila; Derek Tshiabuila; Joseph Tsui; Stephanie van Wyk; Steven Weaver; Constantinos K Wibmer; Eduan Wilkinson; Nicole Wolter; Alexander E Zarebski; Boitumelo Zuze; Dominique Goedhals; Wolfgang Preiser; Florette Treurnicht; Marietje Venter; Carolyn Williamson; Oliver G Pybus; Jinal Bhiman; Allison Glass; Darren P Martin; Andrew Rambaut; Simani Gaseitsiwe; Anne von Gottberg; Tulio de Oliveira
Journal:  Nature       Date:  2022-01-07       Impact factor: 49.962

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