WHO AFRO Region Model Updates for October 2, 2020 Covid.Healthdata

WHO AFRO Region Model updates for October 2, 2020

WHO African Region will see COVID-19 cases and deaths begin to increase in late November, with just over 39,000 deaths by January 1, 2021. If mask use can be increased to 95%, the cumulative death toll by January 1 could decrease to approximately 32,000 saving 7,000 lives.

Current situation

• Regional daily cases have decreased steadily since early August and largely remained below 5,000 for the last few weeks (Figure 1). • Daily deaths appear to match the decrease in cases and remained at about 100 per day for the last few weeks. The largest share of cumulative deaths occurs in ages 55 to 75 (Figures 2a and 2b). • Because of the relatively low levels of death in the region, COVID-19 is likely the 33rd-leading cause of death this week (Table 1). • Effective R is less than 1 across the continent, based on the pooled analysis of cases, hospitalizations (where available), and deaths, showing transmission is declining (Figure 3). • The percentage of infections detected and reported as confirmed cases has remained constant from mid-August at around 9% for the region overall (Figure 5). • South Africa has continued reporting daily death rates of over 1 per million (Figure 6).

Trends in key drivers of transmission (mobility, mask use, testing, and seasonality)

• Most countries have some form of large group gathering restriction in place, except Burundi, Mauritania, Mauritius, Niger, and Nigeria. Still only Eritrea, the Gambia, Guinea-Bissau, and Sierra Leone have mandates for all nonessential business closures. Any business restrictions and school closures remain in place in the majority of countries, while few have maintained stay-at-home orders and travel restrictions. However, many governments are considering lifting these restrictions given the economic and social costs of such measures (Table 2, Figure 7). • Mobility, as measured through smartphone app use, has steadily increased from mid-April, when it was at a level of approximately 40% below the pre-COVID baseline. Overall, regional mobility remained about 6% below the baseline for the last few weeks. Angola, Cabo Verde, and Namibia have the lowest levels of mobility (Figures 8a and 8b). • Mask usage across Africa has remained steady since late May, with approximately 50% of the population reporting always wearing a mask when in public. Mask use is over 70% in much of southern Africa. The lowest levels of mask usage (less than 30%) are seen in Burkina Faso, Cameroon, Mali, and Tanzania (Figures 9a and 9b). • Testing rates in Africa remain the lowest in the world, with a regional average less than 10% per 100,000 people. Only Botswana, Cabo Verde, Gabon, Mauritius, and Namibia test more than 50 people per 100,000 (Figures 10a and 10b).

Projections

• We expect the daily death rate to start increasing in late November, even with mandates being re-imposed by countries when the daily death rate surpasses 8 per million. The peak daily deaths could approach 360 deaths per day by the first week of January if mandates continue to be eased. In the reference scenario, cumulative deaths for the WHO African Region are forecasted to be approximately 39,000 by January 1 (Figures 12 and 13). • If mask use can be increased to 95%, through some combination of mask mandates with penalties and public information campaigns, the cumulative death toll by January could decrease from approximately 39,000 to 32,000 saving about 7,000 lives (Figures 12 and 13).

covid.healthdata.org Institute for Health Metrics and Evaluation WHO AFRO Region Model updates for October 2, 2020

• This forecast assumes that no countries in the region re-implement mandates before January 1, except Cabo Verde, which will need to re-implement mandates by November (Figure 15). • By January 1, we expect South Africa, Eswatini, Gambia, Uganda, and Cabo Verde to have more than 10% of their population infected (Figure 16).

Model updates Predictive validity

We have conducted an 8-week out-of-sample predictive validity experiment where we have tested a large number of parameterizations of the model for each location to identify what have been in the last 8 weeks likely rates of importation of infections. This rate is governed by a parameter in our SEIIR model. We have used the out-of-sample predictive validity testing to select this parameter for each state. Social distancing mandates

In our model to date, we have re-imposed social distancing mandates when the daily death rate reaches 8 per million. This re-imposition has been implanted across the pool of 1,000 models we use for each location using the day the mean across draws reaches 8 per million. To improve accuracy and to better reflect uncertainty on when re-imposition of mandates may occur, we have in this iteration of the model re-imposed mandates at the draw level. In other words, in one of the 1,000 models for each location, when a particular model predicts the daily death rate reaches 8 per million, we re-impose on that particular trajectory the mandates at that point. This means that in aggregate, mandate re- imposition is spread across a range of dates. This shift to using draw level mandate re-imposition has a tendency to decrease our predicted cumulative deaths. Herd immunity

Given considerable public discussion of the role of herd immunity in explaining peaks and subsequent declines in the daily death and case rate, we have explored the implied total death rate for each country based on the infection-fatality rate (IFR) and different assumptions about the level of cumulative infection that will be associated with herd immunity. The natural experiment of the Charles de Gaulle aircraft carrier suggests that up to 70% of individuals can be infected in a situation of near-random mixing. But various theories, including the role of super-spreaders, nonrandom mixing in less dense populations, non-overlapping social networks, and some prior coronavirus immunity, have led to theories that herd immunity may take place at much lower levels of cumulative infection, such as 40% to 60%. Our IFR, based on the analysis of seroprevalence data and herd immunity at 40% cumulative infection, would suggest will eventually see 10,400,000 deaths globally; with herd immunity at 50% cumulative infection, the figure would be 13,100,000 deaths, and at 60% it would be 15,700,000 deaths. Scale-up of a vaccine or improved treatments could substantially reduce these figures. These calculations only serve to suggest that the epidemic is far from complete. In fact, a recent study in Manaus Brazil showed that seroprevalence range between 44% and 66%. The lower estimate of 44% does not account for false-negative cases or antibody waning observed while the upper estimate accounts for both. Therefore, herd immunity is not occurring at low levels of infections and we need to be vigilant until we have an effective and safe vaccine.

Seasonality

Our projections to January 1 take into account the seasonality of COVID-19. The large increase in daily deaths expected in late November and December is driven by continued increases in mobility and declines in mask use, but most importantly by seasonality. We estimate the likely impact of seasonality by examining the trends in the Northern and

covid.healthdata.org Institute for Health Metrics and Evaluation WHO AFRO Region Model updates for October 2, 2020

Southern Hemispheres. For example, Southern Hemisphere countries such as Argentina, Chile, southern Brazil, and South Africa had much larger epidemics than expected on the basis of mobility, testing, and mask use during their winter months. The statistical association between COVID-19 transmission rates and pneumonia seasonality patterns is strong in our data and is the basis for our estimate of the magnitude of the seasonal increase that is expected.

IHME wishes to warmly acknowledge the support of these and others who have made our COVID-19 estimation efforts possible. Thank you.

For all COVID-19 resources at IHME, visit http://www.healthdata.org/covid.

Questions? Requests? Feedback? Please contact us at https://www.healthdata.org/covid/contact-us.

covid.healthdata.org Institute for Health Metrics and Evaluation African Region MODEL UPDATES

COVID-19 Results Brieﬁng: t he A frican Region

Institute for Health Metrics and Evaluation (IHME)

October 02, 2020

This brieﬁng contains s ummary i nformation o n t he l atest p rojections f rom t he I HME m odel o n C OVID-19 in the African Region. The model was run on October 01, 2020.

Model updates

Updates to the model this week include additional data on deaths, cases, and updates on covariates.

covid19.healthdata.org 1 Institute for Health Metrics and Evaluation African Region CURRENT SITUATION

Current situation

Figure 1. Reported daily COVID-19 cases

20,000

15,000

10,000 Count

5,000

Apr May Jun Jul Aug Sep Oct Month

Daily cases

covid19.healthdata.org 2 Institute for Health Metrics and Evaluation African Region CURRENT SITUATION

Table 1. Ranking of COVID-19 among the leading causes of mortality this week, assuming uniform deaths of non-COVID causes throughout the year

Cause name Weekly deaths Ranking Neonatal disorders 14,422 1 Lower respiratory infections 12,732 2 HIV/AIDS 12,224 3 Malaria 11,351 4 Diarrheal diseases 11,088 5 Ischemic heart disease 8,306 6 Stroke 8,063 7 Tuberculosis 7,097 8 Congenital birth defects 3,721 9 Cirrhosis and other chronic liver diseases 3,615 10 COVID-19 749 33

Figure 2a. Reported daily COVID-19 deaths and smoothed trend estimate. Points shown are reported deaths, line and ribbon represent estimate with uncertainty.

600

400 Daily deaths

200

Apr May Jun Jul Aug Sep Oct

covid19.healthdata.org 3 Institute for Health Metrics and Evaluation African Region CURRENT SITUATION

Figure 2b. Estimated cumulative deaths by age group

5 Share of cumulative deaths, % deaths, Share of cumulative

<5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 99 Age group

Figure 3. Mean effective R on September 17, 2020. The estimate of effective R is based on the combined analysis of deaths, case reporting and hospitalizations where available. Current reported cases reflect infections 11-13 days prior so estimates of effective R can only be made for the recent past. Effective R less than 1 means that transmission should decline all other things being held the same.

<0.68 0.68−0.75 0.76−0.83 0.84−0.91 0.92−0.99 1−1.07 1.08−1.15 1.16−1.23 1.24−1.31 >=1.32

covid19.healthdata.org 4 Institute for Health Metrics and Evaluation African Region CURRENT SITUATION

Figure 4. Estimated percent of the population infected with COVID-19 on September 28, 2020

<0.5 0.5−1.4 1.5−2.9 3−3.9 4−5.4 5.5−6.4 6.5−7.9 8−9.4 9.5−10.4 >=10.5

Figure 5. Percent of COVID-19 infections detected. This is estimated as the ratio of reported COVID-19 cases to estimated COVID-19 infections based on the SEIR disease transmission model.

5 Percent of infections detected of infections Percent

0 Feb Mar Apr May Jun Jul Aug Sep Oct

African Region South−East Asia Region Eastern Mediterranean Region Region of the Americas European Region Western Pacific Region

covid19.healthdata.org 5 Institute for Health Metrics and Evaluation African Region CURRENT SITUATION

Figure 6. Daily COVID-19 death rate per 1 million on September 28, 2020

<1 1 to 1.9 2 to 2.9 3 to 3.9 4 to 4.9 5 to 5.9 6 to 6.9 7 to 7.9 >=8

covid19.healthdata.org 6 Institute for Health Metrics and Evaluation African Region CRITICAL DRIVERS

Critical drivers

Table 2. Current mandate implementation All gatherings restricted closed All nonessential businesses restricted businesses Any Mask use School closure home order Stay limits Travel Algeria Angola Benin Botswana Burkina Faso Burundi Cabo Verde Cameroon Central African Republic Chad Comoros Congo Côte d'Ivoire Democratic Republic of the Congo Equatorial Guinea Eritrea Eswatini Ethiopia Gabon Gambia Ghana Guinea Guinea−Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritania Mauritius Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Seychelles Sierra Leone South Africa South Sudan Togo Uganda United Republic of Tanzania Zambia Zimbabwe

Mandate in place No mandate

covid19.healthdata.org 7 Institute for Health Metrics and Evaluation African Region CRITICAL DRIVERS

Figure 7. Total number of social distancing mandates (not including mask use)

Mandate imposition timing Algeria Angola Benin Botswana Burkina Faso Burundi Cabo Verde Cameroon Central African Republic Chad Comoros Congo Côte d'Ivoire Democratic Republic of the Congo Equatorial Guinea Eritrea # of mandates Eswatini Ethiopia 0 Gabon Gambia 1 Ghana Guinea 2 Guinea−Bissau Kenya 3 Lesotho 4 Liberia Madagascar 5 Malawi Mali 6 Mauritania Mauritius 7 Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Seychelles Sierra Leone South Africa South Sudan Togo Uganda United Republic of Tanzania Zambia Zimbabwe Feb Mar Apr May Jun Jul Aug Sep Oct

covid19.healthdata.org 8 Institute for Health Metrics and Evaluation African Region CRITICAL DRIVERS

Figure 8a. Trend in mobility as measured through smartphone app use compared to January 2020 baseline

−25

−50 Percent reduction from average mobility reduction from average Percent Jan Feb Mar Apr May Jun Jul Aug Sep Oct

African Region South−East Asia Region Eastern Mediterranean Region Region of the Americas European Region Western Pacific Region

Figure 8b. Mobility level as measured through smartphone app use compared to January 2020 baseline (percent) on September 28, 2020

=<−50 −49 to −45 −44 to −40 −39 to −35 −34 to −30 −29 to −25 −24 to −20 −19 to −15 −14 to −10 >−10

covid19.healthdata.org 9 Institute for Health Metrics and Evaluation African Region CRITICAL DRIVERS

Figure 9a. Trend in the proportion of the population reporting always wearing a mask when leaving home

20 Percent of population Percent

0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct

African Region South−East Asia Region Eastern Mediterranean Region Region of the Americas European Region Western Pacific Region

Figure 9b. Proportion of the population reporting always wearing a mask when leaving home on September 28, 2020

<30% 30 to 34% 35 to 39% 40 to 44% 45 to 49% 50 to 54% 55 to 59% 60 to 64% 65 to 69% >=70

covid19.healthdata.org 10 Institute for Health Metrics and Evaluation African Region CRITICAL DRIVERS

Figure 10a. Trend in COVID-19 diagnostic tests per 100,000 people

150

100

50 Test per 100,000 population Test

0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct

African Region South−East Asia Region Eastern Mediterranean Region Region of the Americas European Region Western Pacific Region

Figure 10b. COVID-19 diagnostic tests per 100,000 people on September 25, 2020

<5 5 to 9.9 10 to 24.9 25 to 49 50 to 149 150 to 249 250 to 349 350 to 449 450 to 499 >=500

covid19.healthdata.org 11 Institute for Health Metrics and Evaluation African Region CRITICAL DRIVERS

Figure 11. Increase in the risk of death due to pneumonia on February 1 compared to August 1

<−80% −80 to −61% −60 to −41% −40 to −21% −20 to −1% 0 to 19% 20 to 39% 40 to 59% 60 to 79% >=80%

covid19.healthdata.org 12 Institute for Health Metrics and Evaluation African Region PROJECTIONS AND SCENARIOS

Projections and scenarios

We produce three scenarios when projecting COVID-19. The reference scenario is our forecast of what we think is most likely to happen. We assume that if the daily mortality rate from COVID-19 reaches 8 per million, social distancing (SD) mandates will be re-imposed. The mandate easing scenario is what would happen if governments continue to ease social distancing mandates with no re-imposition. The universal mask mandate scenario is what would happen if mask use increased immediately to 95% and social distancing mandates were re-imposed at 8 deaths per million. Figure 12. Cumulative COVID-19 deaths until January 01, 2021 for three scenarios.

40,000 Cumulative deaths per 100,000

3 30,000

2 20,000

10,000 1 Cumulative deaths Cumulative

0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan

Continued SD mandate easing Reference scenario Universal mask use

Fig 13. Daily COVID-19 deaths until January 01, 2021 for three scenarios.

400

0.03 Daily deaths per 100,000 300

0.02 200 Daily deaths 100 0.01

0 0.00 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan

Continued SD mandate easing Reference scenario Universal mask use

covid19.healthdata.org 13 Institute for Health Metrics and Evaluation African Region PROJECTIONS AND SCENARIOS

Fig 14. Daily COVID-19 infections until January 01, 2021 for three scenarios.

400,000 Daily infections per 100,000

30 300,000

20 200,000 Daily infections 100,000 10

0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan

Continued SD mandate easing Reference scenario Universal mask use

covid19.healthdata.org 14 Institute for Health Metrics and Evaluation African Region PROJECTIONS AND SCENARIOS

Fig 15. Month of assumed mandate re-implementation. (Month when daily death rate passes 8 per million, when reference scenario model assumes mandates will be re-imposed.)

September October November December No mandates before Jan 1

covid19.healthdata.org 15 Institute for Health Metrics and Evaluation African Region PROJECTIONS AND SCENARIOS

Figure 16. Forecasted percent infected with COVID-19 on January 01, 2021

<0.5 0.5−1.4 1.5−2.9 3−3.9 4−5.4 5.5−6.4 6.5−7.9 8−9.4 9.5−10.4 >=10.5

Figure 17. Daily COVID-19 deaths per million forecasted on January 01, 2021 in the reference scenario

<1 1 to 1.9 2 to 2.9 3 to 3.9 4 to 4.9 5 to 5.9 6 to 6.9 7 to 7.9 >=8

covid19.healthdata.org 16 Institute for Health Metrics and Evaluation African Region PROJECTIONS AND SCENARIOS

Figure 18. Comparison of reference model projections with other COVID modeling groups. For this comparison, we are including projections of daily COVID-19 deaths from other modeling groups when available: Delphi from the Massachussets Institute of Technology (Delphi; https://www.covidanalytics.io/home), Imperial College London (Imperial; https://www.covidsim.org), The Los Alamos National Laboratory (LANL; https://covid-19.bsvgateway.org/), the SI-KJalpha model from the University of Southern Cal- ifornia (SIKJalpha; https://github.com/scc-usc/ReCOVER-COVID-19), and Youyang Gu (YYG; https: //covid19-projections.com/). Daily deaths from other modeling groups are smoothed to remove inconsistencies with rounding. Regional values are aggregates from availble locations in that region.

600

Models

IHME 400 Delphi Imperial LANL Daily deaths SIKJalpha 200 YYG

0 Oct Nov Dec Jan Date

covid19.healthdata.org 17 Institute for Health Metrics and Evaluation African Region PROJECTIONS AND SCENARIOS

Table 3. Ranking of COVID-19 among the leading causes of mortality in the full year 2020. Deaths from COVID-19 are projections of cumulative deaths on Jan 1, 2021 from the reference scenario. Deaths from other causes are from the Global Burden of Disease study 2019 (rounded to the nearest 100).

Cause name Annual deaths Ranking Neonatal disorders 750,000 1 Lower respiratory infections 662,100 2 HIV/AIDS 635,600 3 Malaria 590,300 4 Diarrheal diseases 576,600 5 Ischemic heart disease 431,900 6 Stroke 419,300 7 Tuberculosis 369,100 8 Congenital birth defects 193,500 9 Cirrhosis and other chronic liver diseases 188,000 10 COVID-19 39,202 33

Mask data source: Premise; Facebook Global symptom survey (This research is based on survey results from University of Maryland Social Data Science Center); Kaiser Family Foundation; YouGov COVID-19 Behaviour Tracker survey A note of thanks: We would like to extend a special thanks to the Pan American Health Organization (PAHO) for key data sources; our partners and collaborators in Argentina, Brazil, Bolivia, Chile, Colombia, Cuba, the Dominican Republic, Ecuador, Egypt, Honduras, Israel, Japan, Malaysia, Mexico, Moldova, Panama, Peru, the Philippines, Russia, Serbia, South Korea, Turkey, and Ukraine for their support and expert advice; and to the tireless data collection and collation efforts of individuals and institutions throughout the world. In addition, we wish to express our gratitude for efforts to collect social distancing policy information in Latin America to University of Miami Institute for Advanced Study of the Americas (Felicia Knaul, Michael Touchton), with data published here: http://observcovid.miami.edu/; Fundación Mexicana para la Salud (Héctor Arreola-Ornelas) with support from the GDS Services International: Tómatelo a Pecho A.C.; and Centro de Investigaciones en Ciencias de la Salud, Universidad Anáhuac (Héctor Arreola-Ornelas); Lab on Research, Ethics, Aging and Community-Health at Tufts University (REACH Lab) and the University of Miami Institute for Advanced Study of the Americas (Thalia Porteny). Further, IHME is grateful to the Microsoft AI for Health program for their support in hosting our COVID-19 data visualizations on the Azure Cloud. We would like to also extend a warm thank you to the many others who have made our COVID-19 estimation efforts possible.

covid19.healthdata.org 18 Institute for Health Metrics and Evaluation