Implementation of the 7-1-7 target for detection, notification, and response to public health threats in five countries: a retrospective, observational study.

BACKGROUND: Suboptimal detection and response to recent outbreaks, including COVID-19 and mpox (formerly known as monkeypox), have shown that the world is insufficiently prepared for public health threats. Routine monitoring of detection and response performance of health emergency systems through timeliness metrics has been proposed to evaluate and improve outbreak preparedness and contain health threats early. We implemented 7-1-7 to measure the timeliness of detection (target of ≤7 days from emergence), notification (target of ≤1 day from detection), and completion of seven early response actions (target of ≤7 days from notification), and we identified bottlenecks to and enablers of system performance. METHODS: In this retrospective, observational study, we conducted reviews of public health events in Brazil, Ethiopia, Liberia, Nigeria, and Uganda with staff from ministries of health and national public health institutes. For selected public health events occurring from Jan 1, 2018, to Dec 31, 2022, we calculated timeliness intervals for detection, notification, and early response actions, and synthesised identified bottlenecks and enablers. We mapped bottlenecks and enablers to Joint External Evaluation (second edition) indicators. FINDINGS: Of 41 public health events assessed, 22 (54%) met a target of 7 days to detect (median 6 days [range 0-157]), 29 (71%) met a target of 1 day to notify (0 days [0-24]), and 20 (49%) met a target of 7 days to complete all early response actions (8 days [0-72]). 11 (27%) events met the complete 7-1-7 target, with variation among event types. 25 (61%) of 41 bottlenecks to and 27 (51%) of 53 enablers of detection were at the health facility level, with delays to notification (14 [44%] of 32 bottlenecks) and response (22 [39%] of 56 bottlenecks) most often at an intermediate public health (ie, municipal, district, county, state, or province) level. Rapid resource mobilisation for responses (six [9%] of 65 enablers) from the national level enabled faster responses. INTERPRETATION: The 7-1-7 target is feasible to measure and to achieve, and assessment with this framework can identify areas for performance improvement and help prioritise national planning. Increased investments must be made at the health facility and intermediate public health levels for improved systems to detect, notify, and rapidly respond to emerging public health threats. FUNDING: Bill & Melinda Gates Foundation.

Enablers and barriers to COVID-19 vaccine uptake in an urban slum in Lagos, Nigeria: informing vaccine engagement strategies for the marginalized.

BACKGROUND: Vaccination against coronavirus disease 2019 (COVID-19) is a cost-effective mitigation strategy against the pandemic. As the COVID-19 vaccine becomes more available, low uptake is now a global threat and understanding the underpinnings in local contexts is a priority for intervention development. We aimed to evaluate behavioural determinants of COVID-19 vaccine acceptance that could inform engagement strategies to improve vaccine uptake in Makoko, an urban slum in Lagos, Nigeria. METHODS: A population-based case-control study utilized the barrier analysis (BA) approach to evaluate the beliefs and behaviours of 45 'doers' and 45 'non-doers'. The standardized BA tabulation sheet was used to assess differences in the proportions between the two groups to identify significant factors that could be addressed through a behaviour change strategy. RESULTS: Perceived social norms (family, friend, healthcare workers) that approve the vaccine and expected vaccine protection against diseases among doers were determinants of behaviour. Perceived poor accessibility, safety concerns, lack of trust, low vaccine efficacy and low susceptibility to the infection were the most important determinants of behaviour among non-doers. CONCLUSIONS: Measures to improve COVID-19 vaccine acceptance in Makoko should include improvement in accessibility and exposing myths and misinformation through clear, concise and evidence-based community education delivered by trusted persons such as healthcare workers and religious leaders.

Sero-surveillance for IgG to SARS-CoV-2 at antenatal care clinics in three Kenyan referral hospitals: Repeated cross-sectional surveys 2020-21.

INTRODUCTION: The high proportion of SARS-CoV-2 infections that have remained undetected presents a challenge to tracking the progress of the pandemic and estimating the extent of population immunity. METHODS: We used residual blood samples from women attending antenatal care services at three hospitals in Kenya between August 2020 and October 2021and a validated IgG ELISA for SARS-Cov-2 spike protein and adjusted the results for assay sensitivity and specificity. We fitted a two-component mixture model as an alternative to the threshold analysis to estimate of the proportion of individuals with past SARS-CoV-2 infection. RESULTS: We estimated seroprevalence in 2,981 women; 706 in Nairobi, 567 in Busia and 1,708 in Kilifi. By October 2021, 13% of participants were vaccinated (at least one dose) in Nairobi, 2% in Busia. Adjusted seroprevalence rose in all sites; from 50% (95%CI 42-58) in August 2020, to 85% (95%CI 78-92) in October 2021 in Nairobi; from 31% (95%CI 25-37) in May 2021 to 71% (95%CI 64-77) in October 2021 in Busia; and from 1% (95% CI 0-3) in September 2020 to 63% (95% CI 56-69) in October 2021 in Kilifi. Mixture modelling, suggests adjusted cross-sectional prevalence estimates are underestimates; seroprevalence in October 2021 could be 74% in Busia and 72% in Kilifi. CONCLUSIONS: There has been substantial, unobserved transmission of SARS-CoV-2 in Nairobi, Busia and Kilifi Counties. Due to the length of time since the beginning of the pandemic, repeated cross-sectional surveys are now difficult to interpret without the use of models to account for antibody waning.

Revealing the extent of the first wave of the COVID-19 pandemic in Kenya based on serological and PCR-test data [version 2;peer review: 2 approved, 1 approved with reservations]

Policymakers in Africa need robust estimates of the current and future spread of SARS-CoV-2. We used national surveillance PCR test, serological survey and mobility data to develop and fit a county-specific transmission model for Kenya up to the end of September 2020, which encompasses the first wave of SARS-CoV-2 transmission in the country. We estimate that the first wave of the SARS-CoV-2 pandemic peaked before the end of July 2020 in the major urban counties, with 30-50% of residents infected. Our analysis suggests, first, that the reported low COVID-19 disease burden in Kenya cannot be explained solely by limited spread of the virus, and second, that a 30-50% attack rate was not sufficient to avoid a further wave of transmission.

Seroprevalence of Antibodies to Severe Acute Respiratory Syndrome Coronavirus 2 Among Healthcare Workers in Kenya.

BACKGROUND: Few studies have assessed the seroprevalence of antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among healthcare workers (HCWs) in Africa. We report findings from a survey among HCWs in 3 counties in Kenya. METHODS: We recruited 684 HCWs from Kilifi (rural), Busia (rural), and Nairobi (urban) counties. The serosurvey was conducted between 30 July and 4 December 2020. We tested for immunoglobulin G antibodies to SARS-CoV-2 spike protein, using enzyme-linked immunosorbent assay. Assay sensitivity and specificity were 92.7 (95% CI, 87.9-96.1) and 99.0% (95% CI, 98.1-99.5), respectively. We adjusted prevalence estimates, using bayesian modeling to account for assay performance. RESULTS: The crude overall seroprevalence was 19.7% (135 of 684). After adjustment for assay performance, seroprevalence was 20.8% (95% credible interval, 17.5%-24.4%). Seroprevalence varied significantly (P < .001) by site: 43.8% (95% credible interval, 35.8%-52.2%) in Nairobi, 12.6% (8.8%-17.1%) in Busia and 11.5% (7.2%-17.6%) in Kilifi. In a multivariable model controlling for age, sex, and site, professional cadre was not associated with differences in seroprevalence. CONCLUSION: These initial data demonstrate a high seroprevalence of antibodies to SARS-CoV-2 among HCWs in Kenya. There was significant variation in seroprevalence by region, but not by cadre.

Comparative performance of WANTAI ELISA for total immunoglobulin to receptor binding protein and an ELISA for IgG to spike protein in detecting SARS-CoV-2 antibodies in Kenyan populations.

Many SARS-CoV-2 antibody detection assays have been developed but their differential performance is not well described. In this study we compared an in-house (KWTRP) ELISA which has been used extensively to estimate seroprevalence in the Kenyan population with WANTAI, an ELISA which has been approved for widespread use by the WHO. Using a wide variety of sample sets including pre-pandemic samples (negative gold standard), SARS-CoV-2 PCR positive samples (positive gold standard) and COVID-19 test samples from different periods (unknowns), we compared performance characteristics of the two assays. The overall concordance between WANTAI and KWTRP was 0.97 (95% CI, 0.95-0.98). For WANTAI and KWTRP, sensitivity was 0.95 (95% CI 0.90-0.98) and 0.93 (95% CI 0.87-0.96), respectively. Specificity for WANTAI was 0.98 (95% CI, 0.96-0.99) and 0.99 (95% CI 0.96-1.00) while KWTRP specificity was 0.99 (95% CI, 0.98-1.00) and 1.00 using pre-pandemic blood donors and pre-pandemic malaria cross-sectional survey samples respectively. Both assays show excellent characteristics to detect SARS-CoV-2 antibodies.

COVID-19 transmission dynamics underlying epidemic waves in Kenya.

Policy decisions on COVID-19 interventions should be informed by a local, regional and national understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission. Epidemic waves may result when restrictions are lifted or poorly adhered to, variants with new phenotypic properties successfully invade, or infection spreads to susceptible subpopulations. Three COVID-19 epidemic waves have been observed in Kenya. Using a mechanistic mathematical model, we explain the first two distinct waves by differences in contact rates in high and low social-economic groups, and the third wave by the introduction of higher-transmissibility variants. Reopening schools led to a minor increase in transmission between the second and third waves. Socioeconomic and urban­rural population structure are critical determinants of viral transmission in Kenya.

Anti-Severe Acute Respiratory Syndrome Coronavirus 2 Immunoglobulin G Antibody Seroprevalence Among Truck Drivers and Assistants in Kenya.

In October 2020, anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immunoglobulin G seroprevalence among truck drivers and their assistants (TDA) in Kenya was 42.3%, higher than among healthcare workers and blood donors. Truck drivers and their assistants transport essential supplies during the coronavirus disease 2019 pandemic, placing them at increased risk of being infected and of transmitting SARS-CoV-2 over a wide geographical area.

Temporal trends of SARS-CoV-2 seroprevalence during the first wave of the COVID-19 epidemic in Kenya.

Observed SARS-CoV-2 infections and deaths are low in tropical Africa raising questions about the extent of transmission. We measured SARS-CoV-2 IgG by ELISA in 9,922 blood donors across Kenya and adjusted for sampling bias and test performance. By 1st September 2020, 577 COVID-19 deaths were observed nationwide and seroprevalence was 9.1% (95%CI 7.6-10.8%). Seroprevalence in Nairobi was 22.7% (18.0-27.7%). Although most people remained susceptible, SARS-CoV-2 had spread widely in Kenya with apparently low associated mortality.

Seroprevalence of anti-SARS-CoV-2 IgG antibodies in Kenyan blood donors.

The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Africa is poorly described. The first case of SARS-CoV-2 in Kenya was reported on 12 March 2020, and an overwhelming number of cases and deaths were expected, but by 31 July 2020, there were only 20,636 cases and 341 deaths. However, the extent of SARS-CoV-2 exposure in the community remains unknown. We determined the prevalence of anti-SARS-CoV-2 immunoglobulin G among blood donors in Kenya in April-June 2020. Crude seroprevalence was 5.6% (174 of 3098). Population-weighted, test-performance-adjusted national seroprevalence was 4.3% (95% confidence interval, 2.9 to 5.8%) and was highest in urban counties Mombasa (8.0%), Nairobi (7.3%), and Kisumu (5.5%). SARS-CoV-2 exposure is more extensive than indicated by case-based surveillance, and these results will help guide the pandemic response in Kenya and across Africa.