[Thread] 1. NEW study on how sick (or not) #Omicron makes people in SA
Full study here: bit.ly/3mo2Y2c (preprint)
2. Cheryl Cohen @nicd_sa: 1. #Omicron emerged in SA when:
- 60-70% of people in SA had been previously infected (so they have natural immunity) 2. Early data suggest less severe disease during the Omicron period
3. #Omicron replaced Delta VERY fast in SA - in Gauteng, where SA's Omicron outbreak started, it replaced Delta within 2 weeks (so it spreads fast).
4. Scientists identified PCR #COVID tests of patients infected with #Omicron.
How did they know it was Omicron?
The PCR test they used looks for 3 things to tell if someone has COVID. 1 of those things = an S gene. People with Omicron normally test negative 4 an S gene.
5. What did scientists look at?
How severe #Omicron infection is.
They compared: 1. SGTF (Omicron) infections with non-SGTF (non-Omicron) infections (1 Oct - 30 Nov) 2. SGTF (Omicron, 1 Oct-30 Nov) infections with Delta infections (April - Nov)
6. Scientists looked at how likely people with #Omicron were to be sick enough to end up in hospital, and, if they were admitted, how likely they were to develop severe disease (end up in ICU, etc). They compared this likelihood to that of non-Omicron and Delta patients.
7. Where did scientists get the test and clinical (how sick they got) data of patients?
From: 1. Real-time COVID case data reported to the NICD 2. Labs (COVID test results + genome data) 2. The DATCOV-Gen network, that links genome data to clinical and hospitalisation data.
8. What were the results?
SGTF (#Omicron) infections vs. non-SGTF (non-Omicron) infections (1 Oct - 30 Nov): 1. Omicron infections = 80% less likely to be admitted to hospital than non-Omicron 2. But Omicron = same chance as non-Omicron patients to fall very ill once in hospital
9. SGTF (Omicron, 1 Oct-30 Nov) infections with Delta infections (April - Nov):
Omicron infections = 70% less likely than Delta infections to develop severe disease (once admitted to hospital)
10. Scientists also looked at something called Ct values. They tell you how much virus you've got in your body. The lower the Ct value the more virus you've got (and more infectious you're likely to be).
11. Scientists found the Ct values of #Omicron (SGTF) infections = lower than those of non-Omicron (non-SGTF) infections. So the viral loads of people with Omicron = likely higher than those infected with other variants, which is probs why Omicron is so much more transmissible.
12. Study limitation:
- Only self-reported vaccination data was available for study participants; many many have had previous undiagnosed infection (so researchers couldn't account for this)
13. The study results are for SA, so a country with high levels of previous infection and relatively low levels of vaccination. Omicron could behave very differently in countries with high vaccination rates and low levels of previous infection.
14. What can this study not tell us? Why #Omicron causes less severe disease in SA.
3 possible reasons: 1. High levels of natural immunity 2. Better vax coverage 4 Omicron vs. Delta wave 3. Omicron = less virulent (makes people less sick)
We need more data to know the reason.
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It’s when health workers try to trace the people 1 infected person could potentially have infected by asking the infected person who they have been in contact with and getting those people to then test or isolate if they test positive.
In short: It’s too expensive for what we get out of it, so we spend a lot of money 4 very little gain (gain = picking up infected people + stopping them from infecting others). We could use the money better on other stuff/diseases.
JUST IN [Thread]: 1. @HealthZA disagrees with the US government's CDC's decision to recommend #mRNA jabs above #JnJ (because of rare side effects) - we'll continue using JnJ 2. The US = abundance of jabs (100 mil + ready for use), so they can afford 2 be choosy.
2. How safe is #JnJ? 1. @MRCza analysed safety data from the #Sisonke study (which uses #JnJ) 2. Serious side effects were rare and occurred in only 129 out of about 500 000 #HealthWorkers in the study
3. What does #Sisonke data tell us about mild #JnJ side effects? 1. The commonest side effects = headache, body aches, pain @ injection site, fever 2. Most side effects = occurred within 48 hours of vaccination
BREAKING [Thread] 1. @SAHPRA1 has approved a 2nd dose of #JnJ and heterologous (mix/match) booster for adults:
1. If u had a JnJ jab, u can get a booster @ least 2 mnths after 1 JnJ shot 2. If u had a #Pfizer jab, u can get a JnJ booster @ least 6 mnths after a 2nd Pfizer jab
2. Does @SAHPRA1's approval mean you can have a #Pfizer booster after a #JnJ shot?
NO. It’s only the other way around (a #JnJ booster after a 2nd #Pfizer shot) that has been approved as a “mix and match” booster.
3. Why does @sahpra1’s “mix and match” approval not allow for a #Pfizer booster after a #JnJ shot?
Pfizer hasn’t submitted data to Sahpra for approval (#JnJ submitted the data 4 a #JnJ booster after #Pfizer).
[Thread] 1. Where does #COVID19 test data in SA come from?
Adrian Puren, @nicd_sa: 1. From people with symptoms who get tested 2. From travelers 3. From both the public and private sector
2. All the #COVID19 testing data is then sent to the NICD and assembled in tables.
3. #COVID19 testing data can be found at these websites:
[Thread] 1. SA's #Omicron#COVID19 wave seems to have turned (cautiously optimistic view). @rid1tweets: 1. Much steeper wave than previous waves, but also much shorter 2. Half the nr of days to reach the Omicron peak vs. peaks of other waves
2. The 7 day moving average of new #COVID19 cases for #Omicron (at what seems/could be the peak of the wave) = 23 4237 vs.
- #Delta peak 19,956
- Beta peak 19,042
- D614G peak 12,584
3. Here are the 7-day moving averages of new #COVID19SA cases, hospitalisations and in-hospital #COVID deaths up until 19 Dec (via @CAPRISAOfficial, @nicd_sa and DATCOV):
- Hospital admissions and deaths, at this stage, still significantly lower than in previous waves.
1. Omicron = detected in 76+ countries 2. All SA's 9 provinces = in 4th wave, although NC is still technically entering its 4th wave (but that's according to a calculation formula, not in practice)
2. #JoePhaahla: 1. Although Gauteng = still highest nr of new #COVID19 cases, all 9 provinces in SA have seen a rapid rise in new cases 2. GP (where SA's #Omicron outbreak started): Thu = 27% of new #COVID19 cases vs. 7-10 days ago, GP cases accounted for 70-80% of new cases
3. #JoePhaahla:
The nr of 4th wave #COVID19 cases has exceeded the peaks of waves 1, 2 (Beta), 3 (#Delta)
By how much?
Wave 1: 21 new cases/100,00 people
Wave 2: 32 new cases/100,000 people
Wave 3: 33 new cases/100,000 people
Wave 4 (#Omicron): 37 new cases/100,000 people