Di Digital Symptom Che hecker an and Trac acin ing Tool l fo - PowerPoint PPT Presentation

Di Digital Symptom Che hecker an and Trac acin ing Tool l fo for SA BASA COVID-19 Project

Con Context A review of the health and economic projections for South Africa indicates the impact on both is very significant and while immediate • steps have to be taken to open the economy, additional steps are required to ensure worker safety and to limit the spread of the virus Current COVID – 19 scenarios (from the Actuarial Society of SA) indicate that most of the SA population will be infected • eventually, resulting in 45,000-90,000 deaths (an annual increase of 10-20%). • Economic impact of remaining in a relatively high level of lockdown (phased lowering based on health projections by province) could result in GVA loss of ~R0.8-1.0tn over a 12 month period or almost 20% reduction in GDP. To avoid this outcome, we are expecting a much faster opening up of the economy. While lockdown has lowered the growth rate of COVID-19 to an R0 of ~1.4 (estimated), this growth rate will still result in close to above • outcomes (albeit lower infection and mortality). Many countries have lowered R0 below 1, effectively suppressing the virus, resulting in an accelerated return to a post-COVID-19 health and economic scenarios. Improved hygiene, social distancing and use of PPE (personal protective equipment), etc. all contribute to lowering transmission. Yet re-opening from lock-down and winter season will increase transmission rates. So more is needed to lower transmission. • The most effective way of lowering transmission is to identify and quarantine/isolate infected and potentially exposed individuals as quickly as possible , which also has by far the lowest financial and social cost. Research* indicates COVID-19 spreading takes place predominantly in pre-symptomatic (45%) and symptomatic (40%) states, with lower transmission from environmental (10%) and asymptomatic (5%) vectors. “You can control the epidemic if you can isolate 60% of patients as soon as they have symptoms, and trace over 50% of their contacts instantaneously and before they infect anybody.” A 30% reduction (50%* 60%) in the current R0 of ~1.4 in SA would be sufficient to stop the growth at this point in time, while a higher success rate would lead to suppression if maintained. Today we will propose the use of Apps and other digital channel technology to achieve this , which has been the way the countries • who have beaten the virus have done it. The role of the employer in adopting technology to ensure safety of the workplace for their employees, customers and other parties is critical to mitigate the risk introduced by covid-19. ​ * Science 2020/04/09/science.abb6936 “Quantifying SARS-COV-2 transmission suggests epidemic control with digital contact tracing”. ​ Source: StatsSA, Actuarial Society of South Africa – COVID outcomes 27 April 2020, FRB Epidemiological modelling; FRB and BSA Economic modelling

spend spe Th The short-te Millions 1,000 1,200 200 400 600 800 nd measur 0 1. Lead Monday declared 15 March - State of Disaster 2. Lead Tuesday 3. Lead Wednesday term economic cost is massive - ~65% sured 4. Lead Thursday 5. Lead Friday d dur Groceries 6. Lead Saturday during 8. Lead Monday - Phase 1 ng Level 5 lockdo 9. Lead Tuesday General Retail 10. Lead Wednesday 23 March - Lockdown announced 11. Lead Thursday 12. Lead Friday Daily Card Issuing Turnover Dining Out & Entertainment 13. Lead Saturday 14. Lead Monday down 15. Lead Tuesday - Phase 2 16. Lead Wednesday n 17. Lead Thursday Tourism 18. Lockdown Friday 27 March - Lockdown effective 19. Lockdown Saturday % drop in point-of 20. Lockdown Monday Fuel 21. Lockdown Tuesday 22. Lockdown Wednesday Automotive 23. Lockdown Thursday 24. Lockdown Friday 25. Lockdown Saturday 26. Lockdown Monday of-sa 27. Lockdown Tuesday sale 28. Lockdown Wednesday 29. Lockdown Thursday 3

The projected mediu Th ium-te term economic impact is massive, even unde under a pr projected d lowered d lockdo down n sc scena nario ​ ESTIMATE IF LOCKDOWN ENDURES ​ GVA change in % ​ Change in GVA per province ​ Apr 20 Jun 20 Aug 20 Oct 20 Dec 20 ​ In % compared to Feb 20 ​ -40 to -49% -30 to -39% -20 to -29% -10 to -19% - 0 to -9% ​ Potential impact on GVA Economic Scenario by Province ​ In R billion ​ -7% ​ 377 400,000 ​ -18% ​ 351 ​ -30% ​ -26% Northern Cape 350,000 ​ -44% ​ 308 Free State ​ 278 ​ 264 300,000 North-West 250,000 ​ 210 Mpumalanga 200,000 Eastern Cape Limpopo 150,000 Western Cape 100,000 KwaZulu-Natal 50,000 Gauteng 0 Feb-20 Mar-20 Apr-20 May-20 Jun-20 Jul-20 Aug-20 Sep-20 Oct-20 Nov-20 Dec-20 Jan-21 Feb-21 Mar-21 Apr 20 Jun 20 Aug 20 Oct 20 Dec 20 ​ Assumptions Eastern Cape L5 L4 L4 L3 L1 Free State L5 L3 L3 L2 L1 Gauteng L5 L3 L4 L3 L1 KwaZulu-Natal L5 L4 L4 L3 L1 Limpopo L5 L3 L3 L2 L1 Mpumalanga L5 L3 L3 L2 L1 Northern Cape L5 L3 L2 L2 L1 North-West L5 L3 L2 L2 L1 Western Cape L5 L4 L5 L4 L2 4 ​ Source: IHS SA 2019 data, FirstRand high frequency economic measurements and economic assumptions, Government published lock-down measures, Epidemiological scenarios

Al All four ur AS ASSA A sc scena narios s pa paint t a bl bleak out utcome with th the the majority ty of SA A po popul pulati tion n be being ng infected Scenario 2 - R0 =2.6, 1.6 (post lockdown); Scenario 1 - Base case R0 =3, 1.8 (post 50% asymptomatic lockdown); 75% asymptomatic Susceptible Exposed Recovered Susceptible Exposed Recovered Dead Cumulative Detected Cumulative Infections Dead Cumulative Detected Cumulative Infections 100,000,000 100,000,000 10,000,000 10,000,000 1,000,000 1,000,000 100,000 100,000 10,000 10,000 1,000 1,000 100 100 10 10 1 1 3/5/20 4/5/20 5/5/20 6/5/20 7/5/20 8/5/20 9/5/20 10/5/20 11/5/20 12/5/20 3/5/20 4/5/20 5/5/20 6/5/20 7/5/20 8/5/20 9/5/20 10/5/20 11/5/20 12/5/20 ​ Source: Actuarial Society of South Africa – COVID outcomes 27 April 2020

Al All four ur AS ASSA A sc scena narios s pa paint t a bl bleak out utcome with th the the majority ty of SA A po popul pulati tion n be being ng infected Scenario 3 - R0 =3.0, 1.5 (post lockdown); Scenario 4 - R0 =3.0, 2.1 (post lockdown); 75% asymptomatic 75% asymptomatic Susceptible Exposed Recovered Susceptible Exposed Recovered Dead Cumulative Detected Cumulative Infections Dead Cumulative Detected Cumulative Infections 100,000,000 100,000,000 10,000,000 10,000,000 1,000,000 1,000,000 100,000 100,000 10,000 10,000 1,000 1,000 100 100 10 10 1 1 3/5/20 4/5/20 5/5/20 6/5/20 7/5/20 8/5/20 9/5/20 10/5/20 11/5/20 12/5/20 3/5/20 4/5/20 5/5/20 6/5/20 7/5/20 8/5/20 9/5/20 10/5/20 11/5/20 12/5/20 ​ Source: Actuarial Society of South Africa – COVID outcomes 27 April 2020

CO COVID-19 o 19 outcomes a are a almost b binary y – if if suppres essed ed, healt ealth outcomes mes ar are e ben enig ign, if if le let run, the e bulk lk of f the e popula latio ion will ill be e in infec ected ed* United States Australia R0: viral reproductive number > 1: broad sustained infection < 1: suppression 450 South African R0 400 However, last 3 weeks consistently indicate higher R0 350 0f ~1.4 300 250 Published case data indicated R0 Initial R0 of 2.7 pre lockdown in line below 1 after lock-down 200 with international experience 150 100 50 0 ​ * SEIR models predict infection growth until Herd Immunity achieved New cases (ex incoming/international) Step-up scenario ​ Source: FirstRand Covid-19 team parameter estimation model – 3 May 2020