Bryan O. Nyawanda

Background: In Kenya, influenza and other respiratory viruses circulate year‐round. The Ministry of Health monitors severe acute respiratory infections (SARI), including influenza‐associated hospitalizations, through a sentinel surveillance network. We developed intensity thresholds (ITs) to assess seasonal severity based on respiratory illness‐ and influenza‐associated hospitalizations. Methods: Nine surveillance sites contributed data from January 2019 through December 2025 (with rolling start dates). Weekly all‐cause admissions, respiratory illnesses, and influenza testing in pediatric (0–13 years) and adult (≥ 14 years) medical wards were collected using retrospective record review (prior to June 2023) and SARI surveillance platform reporting (June 2023 onwards). Four sites had sufficient historical data to calculate ITs for the 2024 assessment; eight sites were included for 2025. We used the moving epidemic method to calculate annual and biannual ITs for moderate (50th percentile; IT ₅₀ ), high (IT ₉₀ ), and very high (IT ₉₈ ) severity for the percentage of hospitalizations for respiratory illness and influenza, overall and by age group. Results: During 2024 and 2025, respiratory illness–associated hospitalizations remained below the IT ₅₀ for most of the year across age groups, with 1–2 weeks above the IT ₅₀ . Influenza‐associated hospitalizations also remained below the annual IT ₅₀ in both years but exceeded the biannual IT ₅₀ for several weeks at each wave's peak. Conclusions: In 2024 and 2025, respiratory illness–associated and influenza‐associated hospitalization severity was low to moderate. This approach can guide public health activities in Kenya, contribute to global influenza surveillance, and provide a framework to assess influenza severity in countries without clear Northern or Southern Hemisphere seasonality.

Background: Most maternal SARS-CoV-2 infections are non-severe, yet the maternal–neonatal immune consequences of non-severe infection remain less defined, particularly in African maternal–newborn cohorts that remain underrepresented in immunologic studies of emerging infections. We investigated whether non-severe maternal SARS-CoV-2 infection altered maternal and cord blood immune mediator profiles in a prospective Kenyan pregnancy cohort.Methods: In a prospective cohort at two Kenyan hospitals before vaccine rollout, pregnant women enrolled at less than 31 weeks’ gestation were followed to delivery. Of 430 enrolled participants (298 eligible), we profiled all 8 symptomatic SARS-CoV-2 cases together with 13 randomly selected controls and 13 randomly selected asymptomatic sero-converters. We measured 39 immune mediators and combined biomarker-level analyses of maternal trajectories and adjusted cord blood differences with profile-level analyses of maternal–cord compartmentalization.Findings: Acute symptomatic COVID-19 was associated with elevated IP-10, IL-1RA, GRO-α, MCP-1, M-CSF, IL-7, IL-18, IL-25, PDGF-AA, and PDGF-AB/BB from baseline. Most elevations were resolved by convalescence, but RANTES and GRO-α fell below enrollment levels. Maternal and cord blood profiles remained distinct, but COVID-19 altered compartmentalization. After adjustment for maternal concentrations, cord blood IL-12p40, EGF, G-CSF, M-CSF, IL-8, and Eotaxin were lower in COVID-19 than in control dyads.Interpretation: Non-severe maternal SARS-CoV-2 infection elicited a selective acute response engaging chemokine, regulatory, and growth factor pathways, without broad hypercytokinaemia, followed by chemokine suppression at convalescence and attenuated cord concentrations of selected immune mediators at delivery. These findings suggest that pathogens that perturb the maternal–fetal interface may introduce vulnerabilities not apparent through routine case-based surveillance and could inform maternal and early-life protection strategies in future outbreaks.

Background Acute respiratory illnesses (ARI) are a major cause of morbidity and mortality globally. With (re)emergence of novel viruses and increased access to childhood bacterial vaccines, viruses have assumed greater importance in the aetiology of ARI. There are now promising candidate vaccines against some of the most common endemic respiratory viruses. Optimal delivery strategies for these vaccines, and the need for interventions against other respiratory viruses, requires geographically diverse data capturing temporal variations in virus circulation. Methods We leveraged three health facility-based respiratory illness surveillance platforms operating in 11 sites across Kenya. Nasopharyngeal (NP) and/or oropharyngeal (OP) specimens, patient demographic, and clinical characteristics were collected in 2014 from individuals of various ages presenting with respiratory symptoms at the surveillance facilities. Real time multiplex polymerase chain reaction was used to detect rhinoviruses, respiratory syncytial virus (RSV), influenza virus, human coronaviruses (hCoV), and adenoviruses. Results From 11 sites, 5451 NP/OP specimens were collected and tested from patients. Of these, 40.2% were positive for at least one of the targeted respiratory viruses. The most frequently detected were rhinoviruses (17.0%) and RSV A/B (10.5%), followed by influenza A (6.2%), adenovirus (6.0%) and hCoV (4.2%). RSV was most prevalent among infants aged <12 months old (18.9%), adenovirus among children aged 12–23 months old (11.0%), influenza A among children aged 24–59 months (9.3%), and rhinovirus across all age groups (range, 12.7–19.0%). RSV had a higher virus positivity in the inpatient setting (12.5%) compared to outpatient setting (4.8%). The overall percent virus positivity varied by surveillance site, health facility type and case definition used in surveillance. Conclusions We identify rhinoviruses, RSV, and influenza A as the most prevalent respiratory viruses. Higher RSV positivity in inpatients, and in infants, strengthens the case for RSV vaccination. To inform the design and delivery of public health interventions, long-term surveillance is required to establish regional heterogeneities in respiratory virus circulation and seasonality.

Malaria control in sub-Saharan Africa lies at the intersection of changing climate suitability and the scale-up of vector control and case management. Drawing on recent evidence from Kenya, we argue that climate variability already exerts effects on malaria outcomes comparable to, and sometimes stronger than, those of commonly measured interventions at local scales. Transmission dynamics display non-linear, lagged relationships with temperature and rainfall. As a result, climate change is expected to alter prevailing conditions and extremes, reshaping the geography and seasonality of malaria risk. At the same time, socio-economic development and vector control intervention such as insecticide-treated bed nets (ITNs) and timely case management continue to reduce malaria incidence and deaths, especially among young children. However, their population-level impact depends on when and where interventions are deployed relative to climate-favoured windows of transmission. We propose a practical agenda for “climate-smart” malaria control in Kenya advocating for dynamic targeting of interventions according to observed climate lags and thresholds, sustaining protection for the youngest, and innovating approaches for school-age reservoirs of infection. Access to effective care should be re-conceptualized as a climate-adaptation strategy, and short-term, locally tailored forecasts should be embedded into routine planning to support anticipatory and equitable malaria control.

Malaria is a vector-borne disease, subject to climate change. The true impact of climate change on malaria dynamics is, however, still debated. Between 2008-2019, we studied patterns of malaria dynamics in a lowland area of western Kenya. We used wavelet analysis to assess the seasonality of monthly malaria incidence and related climatic factors, including air temperature, land surface temperature, rainfall and Niño 3.4 sea surface temperature. We performed a maximal overlap discrete wavelet transform to decompose incidence and climatic factors and fitted bivariate linear regressions to analyse their relationships across time scales. We observed a strong semestrial seasonality of malaria with the emergence of an annual cycle with variation strongly associated with rainfall dynamics. Rainfall emerged as a significant short-term predictor, while temperature contributed more at higher time scales. We found a recent increase in the time lag between climatic factors and their related effects on malaria incidence. This augmentation is related to bed net coverage and El Niño events. Our study underlines the importance of considering long-term time scales when assessing malaria dynamics. The presented wavelet approach could be applicable to other infectious diseases.

Malaria is a vector-borne disease, subject to climate change. The true impact of climate change on malaria dynamics is, however, still debated. Between 2008-2019, we studied patterns of malaria dynamics in a lowland area of western Kenya. We used wavelet analysis to assess the seasonality of monthly malaria incidence and related climatic factors, including air temperature, land surface temperature, rainfall and Niño 3.4 sea surface temperature. We performed a maximal overlap discrete wavelet transform to decompose incidence and climatic factors and fitted bivariate linear regressions to analyse their relationships across time scales. We observed a strong semestrial seasonality of malaria with the emergence of an annual cycle with variation strongly associated with rainfall dynamics. Rainfall emerged as a significant short-term predictor, while temperature contributed more at higher time scales. We found a recent increase in the time lag between climatic factors and their related effects on malaria incidence. This augmentation is related to bed net coverage and El Niño events. Our study underlines the importance of considering long-term time scales when assessing malaria dynamics. The presented wavelet approach could be applicable to other infectious diseases.

The substantial decline observed in the global trends of malaria incidence since the year 2000, largely attributable to the scale-up of malaria control interventions, has been stagnating since 2020, with the burden remaining disproportionately high in sub-Saharan Africa [1]. Changes in climatic factors threaten to expand the transmission areas and further reverse the gains made […]

Background Malaria mortality is influenced by several factors including climatic and environmental factors, interventions, socioeconomic status (SES) and access to health systems. Here, we investigated the joint effects of climatic and non-climatic factors on under-five malaria mortality at different spatial scales using data from a Health and Demographic Surveillance System (HDSS) in western Kenya. Methods We fitted Bayesian spatiotemporal (zero-inflated) negative binomial models to monthly mortality data aggregated at the village scale and over the catchment areas of the health facilities within the HDSS, between 2008 and 2019. First order autoregressive temporal and conditional autoregressive spatial processes were included as random effects to account for temporal and spatial variation. Remotely sensed climatic and environmental variables, bed net use, SES, travel time to health facilities, proximity from water bodies/streams and altitude were included in the models to assess their association with malaria mortality. Results Increase in rainfall (mortality rate ratio (MRR)=1.12, 95% Bayesian credible interval (BCI): 1.04–1.20), Normalized Difference Vegetation Index (MRR=1.16, 95% BCI: 1.06–1.28), crop cover (MRR=1.17, 95% BCI: 1.11–1.24) and travel time to the hospital (MRR=1.09, 95% BCI: 1.04–1.13) were associated with increased mortality, whereas increase in bed net use (MRR=0.84, 95% BCI: 0.70–1.00), distance to the nearest streams (MRR=0.89, 95% BCI: 0.83–0.96), SES (MRR=0.95, 95% BCI: 0.91–1.00) and altitude (MRR=0.86, 95% BCI: 0.81–0.90) were associated with lower mortality. The effects of travel time and SES were no longer significant when data was aggregated at the health facility catchment level. Conclusion Despite the relatively small size of the HDSS, there was spatial variation in malaria mortality that peaked every May–June. The rapid decline in malaria mortality was associated with bed nets, and finer spatial scale analysis identified additional important variables. Time and spatially targeted control interventions may be helpful, and fine spatial scales should be considered when data are available.

Respiratory syncytial virus (RSV) is a major cause of lower respiratory tract infection (LRTI) among infants under 6 months of age. Yet, in Kenya, little is known about healthcare workers’ (HCWs) knowledge, attitudes, and perceptions around RSV disease and the prevention products under development. Between September and October 2021, we conducted a mixed methods cross-sectional survey to assess HCWs’ knowledge, attitudes, and perceptions of RSV disease and RSV vaccinations in two counties. We enrolled HCWs delivering services directly at maternal and child health (MCH) departments in selected health facilities (frontline HCWs) and health management officers (HMOs). Of the 106 respondents, 94 (88.7%) were frontline HCWs, while 12 were HMOs. Two of the HMOs were members of the Kenya National Immunization Technical Advisory Group (KENITAG). Of the 104 non-KENITAG HCWs, only 41 (39.4%) had heard about RSV disease, and 38/41 (92.7%) felt that pregnant women should be vaccinated against RSV. Most participants would recommend a single-dose vaccine schedule (n = 62, 58.5%) for maximal adherence and compliance (n = 38/62, 61.3%), single dose/device vaccines (n = 50/86, 58.1%) to prevent wastage and contamination, and maternal vaccination through antenatal care clinics (n = 53, 50%). We found the need for increased knowledge about RSV disease and prevention among Kenyan HCWs.