Investment in SARS-CoV-2 sequencing in Africa over the past year has led to a major increase in the number of sequences generated, now exceeding 100,000 genomes, used to track the pandemic on the continent. Our results show an increase in the number of African countries able to sequence domestically, and highlight that local sequencing enables faster turnaround time and more regular routine surveillance. Despite limitations of low testing proportions, findings from this genomic surveillance study underscore the heterogeneous nature of the pandemic and shed light on the distinct dispersal dynamics of Variants of Concern, particularly Alpha, Beta, Delta, and Omicron, on the continent. Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve, while the continent faces many emerging and re-emerging infectious disease threats. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century.

This document is intended to provide information to public health and clinical professionals and does not supersede any provincial/territorial legislative, regulatory, policy and practice requirements or professional guidelines that govern the practice of health professionals in their respective jurisdictions, whose recommendations may differ due to local epidemiology or context.

Background:The International Health Regulations state that early detection and immediate reporting of unusual health events is important for early warning and response systems.Objective: To describe a pilot surveillance program established in health facilities in Yaoundé, Cameroon in 2017 which aimed to enable detection and reporting of public health events.Health Laboratory, implemented event-based surveillance (EBS) in nine Yaoundé health facilities. Four signals were defined that could indicate possible public health events, and a reporting, triage, and verification system was established among partner organizations. A pre-defined laboratory algorithm was defined, and a series of workshops trained health facilities, laboratory, and public health staff for surveillance implementation. Results: From May 2017 to January 2018, 30 signals were detected, corresponding to 15 unusual respiratory events. All health facilities reported a signal at least once, and more than three-quarters of health facilities reported ≥2 times. Among specimens tested, the pathogens detected included Klebsiella pneumoniae, Moraxella catarrhalis, Streptococcus pneumoniae, Haemophilus influenza, Staphylococcus aureus, Pneumocystis jiroveci, influenza A (H1N1) virus, rhinovirus, and adenovirus. Conclusions:The events detected in this pilot were caused by routine respiratory bacteria and viruses, and no novel influenza viruses or other emerging respiratory threats were identified. The surveillance system, however, strengthened relationships and communication linkages between health facilities and public health authorities. | 123ALROY et AL.

Table S1. Distribution of study population characteristics CURY HCY HLD Whole Study % (n) % (n) % (n) % (n) Patient recruitment No. of invited patients 123 119 135 377 Participation rate 91.1(112) 92.4 (110) 94.8 (128) 92.8 (377) Gender Female 8.0 (9) 2.7 (3) 17.2 (22) 9.7 (34) Male 92.0 (103) 97.3 (107) 82.8 (106) 90.3 (316) Age group <18 years 2.7 (3) 1.8 (2) 1.6 (2) 2.0 (7) 18-29 years 37.5 (42) 42.7 (47) 31.3 (40) 36.9 (129) 30-39 years 35.7 (40) 27.3 (30) 37.5 (48) 33.7 (118) 40-49 years 12.5 (14) 15.5 (17) 20.3 (26) 16.3 (57) 50+ years 11.6 (13) 12.7 (14) 9.4 (12) 11.1 (39) Education level No 4.5 (5) 3.6 (4) 3.9 (5) 4.0 (14) Primary 24.1 (27) 20.0 (22) 28.1 (36) 24.3 (85) Secondary 60.7 (68) 60.0 (66) 54.7 (70) 58.3 (204) Tertiary 10.7 (12) 16.4 (18) 13.3 (17) 13.4 (47) Religion Christian 74.1 (83) 82.7 (91) 79.7 (102) 78.9 (276) Muslim 17.9 (20) 10.9 (12) 10.2 (13) 12.9 (45) Other 8.0 (9) 6.4 (7) 10.2 (13) 8.3 (29) Data collection time intervals Working days 06-17:59 28.6 (32) 39.1 (43) 25.8 (33) 30.9 (108) Working days 18-05:59 25.0 (28) 30.0 (33) 25.0 (32) 26.6 (93) Weekend 06-17:59 17.0 (19) 9.1 (10) 21.9 (28) 16.3 (57) Weekend 18-05:59 29.5 (33) 21.8 (24) 27.3 (35) 26.3 (92) Type of victim Motorcycle riders a 58.9 (66) 66.4 (73) 47.7 (61) 57.1 (200) Drivers b 13.4 (15) 10.9 (12) 7.8 (10) 10.6 (37) Pedestrians 27.7 (31) 22.7 (25) 44.5 (57) 32.3 (113) Vehicle types involved Motorcycle 69.6 (78) 71.8 (79) 81.1 (104) 74.6 (261) Car/van 18.8 (21) 12.7 (14) 7.8 (10) 12.9 (45) Bus/taxi 4.5 (5) 7.3 (8) 6.3 (8) 6.0 (21) Truck 3.6 (4) 7.3 (8) 3.9 (5) 4.9 (17) "Clando" 3.6 (4) 0.9 (1) 0.8 (1) 1.7 (6) Professional driver or rider 73.2 (82) 69.1 (76) 92.2 (118) 78.9 (276) (Continues)Table S1 (continued). Distribution of study population characteristics CURY HCY HLD Whole Study % (n) % (n) % (n) % (n) Collision characteristics MC c alone 3.6 (4) 6.4 (7) 1.6 (2) 3.7 (13) MC vs MC 11.6 (13) 14.5 (16) 13.3 (17) 13.1 (46) MC vs pedestrian 18.8 (21) 10.0 (11) 40.6 (52) 24.1 (84) Car d alone 4.5 (5) 0.9 (1) 0.8 (1) 2.0 (7) Car vs MC 41.1 (46) 44.5 (49) 26.6 (34) 36.9 (129) Car vs car 3.6 (4) 3.6 (4) 5.5 (7) 4.3 (15) Car vs pedestrian 16.1 (18) 19.1 (21) 11.7 (15) 15.4 (55) Skidding 0.9 (1) 0.9 (1) 0.0 (0) .6 (2) Type of collision Collison at junction 23.2 (26) 26.4 (29) 7.8 (10) 18.6 (65) Skidding 8.0 (9) 8.2 (9) 1.6 (2) 5.7 (20) Head-on 30.4 (34) 31.8 (35) 63.3 (81) 42.9 (150) Other 0.9 (1) 0.0 (0) 0.0 (0) .3 (1) Rear end 31.3 (35) 30.9 (34) 11.7 (15) 24.0 (84) Roadway departure 6.3 (7) 2.7 (3) 15.6 (20) 8.6 (30) Type of injuries Abdomen or pelvic contents 15.2 (17) 13.6 (15) 9.4 (12) 12.6 (44) Chest 2.7 (3) 3.6 (4) 2.3 (3) 2.9 (10) Extremities or pelvic girdle 41.1 (46) 37.3 (41) 37.5 (48) 38.6 (135) Face 8.9 (10) 4.5 (5) 6.3 (8) 6.6 (23) Head and neck 28.6 (32) 33.6 (37) 43.0 (55) 35.4 (124) No injury 3.6 (4) 7.3 (8) 1.6 (2) 4.4 (14) Severity of injuries Critical 21.4 (24) 18.2 (20) 8.6 (11) 15.7 (55) Severe 55.4 (62) 50.9 (56) 30.5 (39) 44.9 (157) Serious 10.7 (12) 13.6 (15) 26.6 (34) 17.4 (61) Moderate 6.3 (7) 5.5 (6...

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