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Background: Little is known about the cocirculation of influenza and SARS-CoV-2 viruses during the COVID-19 pandemic and the use of respiratory disease sentinel surveillance platforms for monitoring SARS-CoV-2 activity in sub-Saharan Africa., Objective: We aimed to describe influenza and SARS-CoV-2 cocirculation in Kenya and how the SARS-CoV-2 data from influenza sentinel surveillance correlated with that of universal national surveillance., Methods: From April 2020 to March 2022, we enrolled 7349 patients with severe acute respiratory illness or influenza-like illness at 8 sentinel influenza surveillance sites in Kenya and collected demographic, clinical, underlying medical condition, vaccination, and exposure information, as well as respiratory specimens, from them. Respiratory specimens were tested for influenza and SARS-CoV-2 by real-time reverse transcription polymerase chain reaction. The universal national-level SARS-CoV-2 data were also obtained from the Kenya Ministry of Health. The universal national-level SARS-CoV-2 data were collected from all health facilities nationally, border entry points, and contact tracing in Kenya. Epidemic curves and Pearson r were used to describe the correlation between SARS-CoV-2 positivity in data from the 8 influenza sentinel sites in Kenya and that of the universal national SARS-CoV-2 surveillance data. A logistic regression model was used to assess the association between influenza and SARS-CoV-2 coinfection with severe clinical illness. We defined severe clinical illness as any of oxygen saturation <90%, in-hospital death, admission to intensive care unit or high dependence unit, mechanical ventilation, or a report of any danger sign (ie, inability to drink or eat, severe vomiting, grunting, stridor, or unconsciousness in children younger than 5 years) among patients with severe acute respiratory illness., Results: Of the 7349 patients from the influenza sentinel surveillance sites, 76.3% (n=5606) were younger than 5 years. We detected any influenza (A or B) in 8.7% (629/7224), SARS-CoV-2 in 10.7% (768/7199), and coinfection in 0.9% (63/7165) of samples tested. Although the number of samples tested for SARS-CoV-2 from the sentinel surveillance was only 0.2% (60 per week vs 36,000 per week) of the number tested in the universal national surveillance, SARS-CoV-2 positivity in the sentinel surveillance data significantly correlated with that of the universal national surveillance (Pearson r=0.58; P<.001). The adjusted odds ratios (aOR) of clinical severe illness among participants with coinfection were similar to those of patients with influenza only (aOR 0.91, 95% CI 0.47-1.79) and SARS-CoV-2 only (aOR 0.92, 95% CI 0.47-1.82)., Conclusions: Influenza substantially cocirculated with SARS-CoV-2 in Kenya. We found a significant correlation of SARS-CoV-2 positivity in the data from 8 influenza sentinel surveillance sites with that of the universal national SARS-CoV-2 surveillance data. Our findings indicate that the influenza sentinel surveillance system can be used as a sustainable platform for monitoring respiratory pathogens of pandemic potential or public health importance., (©Daniel Owusu, Linus K Ndegwa, Jorim Ayugi, Peter Kinuthia, Rosalia Kalani, Mary Okeyo, Nancy A Otieno, Gilbert Kikwai, Bonventure Juma, Peninah Munyua, Francis Kuria, Emmanuel Okunga, Ann C Moen, Gideon O Emukule. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 25.03.2024.)

Selection based on genomic predictions has become the method of choice for genetic improvement in dairy cattle. This offers huge opportunity for developing countries with little or no pedigree data, and preliminary studies have shown promising results. The African Dairy Genetic Gains (ADGG) project initiated a digital system of dairy performance data collection, accompanied by genotyping in Tanzania in 2016. Currently, ADGG has the largest body of dairy performance data generated in East Africa from a smallholder dairy system. This study examines the use of genomic best linear unbiased prediction (GBLUP) and single-step (ss)GBLUP for the estimation of genetic parameters and accuracy of genomic prediction for daily milk yield and body weight in Tanzania. The estimates of heritability for daily milk yield from GBLUP and ssGBLUP were essentially the same, at 0.12 ± 0.03. The heritability estimates for daily milk yield averaged over the whole lactation from random regression model (RRM) GBLUP or ssGBLUP were 0.22 and 0.24, respectively. The heritability of body weight from GBLUP was 0.24 ± 04 but was 0.22 ± 04 from the ssGBLUP analysis. Accuracy of genomic prediction for milk yield from a forward validation was 0.57 for GBLUP based on fixed regression model or 0.55 from an RRM. Corresponding estimates from ssGBLUP were 0.59 and 0.53, respectively. Accuracy for body weight, however, was much higher at 0.83 from GBLUP and 0.77 for ssGBLUP. The moderate to high levels of accuracy of genomic prediction (0.53-0.83) obtained for milk yield and body weight indicate that selection on the basis of genomic prediction is feasible in smallholder dairy systems and most probably the only initial possible pathway to implementing sustained genetic improvement programs in such systems., (© 2021, The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).)

Background: Influenza surveillance helps time prevention and control interventions especially where complex seasonal patterns exist. We assessed influenza surveillance sustainability in Africa where influenza activity varies and external funds for surveillance have decreased., Methods: We surveyed African Network for Influenza Surveillance and Epidemiology (ANISE) countries about 2011-2017 surveillance system characteristics. Data were summarized with descriptive statistics and analyzed with univariate and multivariable analyses to quantify sustained or expanded influenza surveillance capacity in Africa., Results: Eighteen (75%) of 24 ANISE members participated in the survey; their cumulative population of 710 751 471 represent 56% of Africa's total population. All 18 countries scored a mean 95% on WHO laboratory quality assurance panels. The number of samples collected from severe acute respiratory infection case-patients remained consistent between 2011 and 2017 (13 823 vs 13 674 respectively) but decreased by 12% for influenza-like illness case-patients (16 210 vs 14 477). Nine (50%) gained capacity to lineage-type influenza B. The number of countries reporting each week to WHO FluNet increased from 15 (83%) in 2011 to 17 (94%) in 2017., Conclusions: Despite declines in external surveillance funding, ANISE countries gained additional laboratory testing capacity and continued influenza testing and reporting to WHO. These gains represent important achievements toward sustainable surveillance and epidemic/pandemic preparedness., (© 2020 The Authors. Influenza and Other Respiratory Viruses published by John Wiley & Sons Ltd.)

Respiratory pathogens, such as novel influenza A viruses, Middle East respiratory syndrome coronavirus (MERS-CoV), and now, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), are of particular concern because of their high transmissibility and history of global spread (1). Clusters of severe respiratory disease are challenging to investigate, especially in resource-limited settings, and disease etiology often is not well understood. In 2014, endorsed by the Group of Seven (G7),* the Global Health Security Agenda (GHSA) was established to help build country capacity to prevent, detect, and respond to infectious disease threats. † GHSA is a multinational, multisectoral collaboration to support countries towards full implementation of the World Health Organization's International Health Regulations (IHR). § Initially, 11 technical areas for collaborator participation were identified to meet GHSA goals. CDC developed the Detection and Response to Respiratory Events (DaRRE) strategy in 2014 to enhance country capacity to identify and control respiratory disease outbreaks. DaRRE initiatives support the four of 11 GHSA technical areas that CDC focuses on: surveillance, laboratory capacity, emergency operations, and workforce development. ¶ In 2016, Kenya was selected to pilot DaRRE because of its existing respiratory disease surveillance and laboratory platforms and well-developed Field Epidemiology and Laboratory Training Program (FELTP) (2). During 2016-2020, Kenya's DaRRE partners (CDC, the Kenya Ministry of Health [MoH], and Kenya's county public health officials) conceptualized, planned, and implemented key components of DaRRE. Activities were selected based on existing capacity and determined by the Kenya MoH and included 1) expansion of severe acute respiratory illness (SARI) surveillance sites; 2) piloting of community event-based surveillance; 3) expansion of laboratory diagnostic capacity; 4) training of public health practitioners in detection, investigation, and response to respiratory threats; and 5) improvement of response capacity by the national emergency operations center (EOC). Progress on DaRRE activity implementation was assessed throughout the process. This pilot in Kenya demonstrated that DaRRE can support IHR requirements and can capitalize on a country's existing resources by tailoring tools to improve public health preparedness based on countries' needs., Competing Interests: All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. No potential conflicts of interest were disclosed.