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Abstract Background Lymphatic filariasis (LF) is an infectious neglected tropical disease caused by mosquito-borne nematodes such as Wuchereria bancrofti, Brugia malayi, and Brugia timori. Globally, LF affects 51 million people, with approximately 863 million at risk in 47 countries. In Kenya, filariasis is endemic along the entire coastal strip, and more recently, at the Kenya–Ugandan border. The World Health Organization (WHO) recommends mass drug administration to reduce disease transmission and morbidity. Monitoring the effectiveness of such interventions relies on robust surveillance, achieved through microscopic examination of microfilariae in nighttime blood, detection of circulating filarial antigens (CFA), and molecular xenomonitoring. We focused on molecular xenomonitoring along the Kenyan coast due to its noninvasive nature and the opportunity to identify new vectors. Methods In 2022, mosquitoes were collected from Kilifi, Kwale, and Taita-Taveta counties located within the LF endemic region in Kenya. Subsequently, genomic deoxyribonucleic acid (gDNA) was extracted from these mosquitoes for speciation and analysis of Wuchereria bancrofti infection rates. The impact of sociodemographic and household attributes on infection rates was assessed using generalized estimating equations. Results A total of 18,121 mosquitoes belonging to Culicinae (63.0%, n = 11,414) and Anophelinae (37.0%, n = 6707) subfamilies were collected. Morphological identification revealed that Anopheline mosquitoes were dominated by An. funestus (45.4%, n = 3045) and An. gambiae (42.8%, n = 2873). Wuchereria bancrofti infection rates were highest in Kilifi (35.4%; 95% CI 28.0–43.3%, n = 57/161) and lowest in Taita Taveta (5.3%; 95% CI 3.3–8.0%, n = 22/412). The major vectors incriminated are An. rivulorum, An. funestus sensu stricto, and An. arabiensis. Mosquitoes of the An. funestus complex were significantly associated with LF transmission (OR 18.0; 95% CI 1.80–180; p = 0.014). Additionally, a higher risk of transmission was observed outdoors (OR 1.74; 95% CI 1.08–2.82; p = 0.024) and in homesteads that owned livestock (OR 2.00; 95% CI 1.09–3.66; p = 0.025). Conclusions In this study, we identified An. funestus s.l. sibling species, An. rivulorum and An. funestus s.s., as the primary vectors of lymphatic filariasis along the Kenyan coast. These findings also highlight that a significant portion of disease transmission potentially occurs outdoors where indoor-based vector control tools, including long-lasting insecticidal nets and indoor residual spray, may not be effective. Therefore, control measures targeting outdoor resting mosquitoes such as zooprophylaxis, larval source management, and attractive sugar baits may have potential for LF transmission reduction. Graphical Abstract

Abstract Background Understanding spatial variations in health outcomes is a fundamental component in the design of effective, efficient public health strategies. Here we analyse the spatial heterogeneity of low birthweight (LBW) hospital deliveries from a demographic surveillance site on the Kenyan coast. Methods A secondary data analysis on singleton livebirths that occurred between 2011 and 2021 within the rural areas of the Kilifi Health and demographic surveillance system (KHDSS) was undertaken. Individual-level data was aggregated at enumeration zone (EZ) and sub-location level to estimate the incidence of LBW adjusted for accessibility index using the Gravity model. Finally, spatial variations in LBW were assessed using Martin Kulldorf’s spatial scan statistic under Discrete Poisson distribution. Results Access adjusted LBW incidence was estimated as 87 per 1,000 person years in the under 1 population (95% CI: 80, 97) at the sub-location level similar to EZ. The adjusted incidence ranged from 35 to 159 per 1,000 person years in the under 1 population at sub-location level. There were six significant clusters identified at sub-location level and 17 at EZ level using the spatial scan statistic. Conclusions LBW is a significant health risk on the Kenya coast, possibly under-estimated from previous health information systems, and the risk of LBW is not homogenously distributed across areas served by the County hospital.

Abstract Background Understanding the age patterns of disease is necessary to target interventions to maximise cost-effective impact. New malaria chemoprevention and vaccine initiatives target young children attending routine immunisation services. Here we explore the relationships between age and severity of malaria hospitalisation versus malaria transmission intensity. Methods Clinical data from 21 surveillance hospitals in East Africa were reviewed. Malaria admissions aged 1 month to 14 years from discrete administrative areas since 2006 were identified. Each site-time period was matched to a model estimated community-based age-corrected parasite prevalence to provide predictions of prevalence in childhood (PfPR2–10). Admission with all-cause malaria, severe malaria anaemia (SMA), respiratory distress (RD) and cerebral malaria (CM) were analysed as means and predicted probabilities from Bayesian generalised mixed models. Results 52,684 malaria admissions aged 1 month to 14 years were described at 21 hospitals from 49 site-time locations where PfPR2–10 varied from

Abstract Background The over-distributed pattern of malaria transmission has led to attempts to define malaria “hotspots” that could be targeted for purposes of malaria control in Africa. However, few studies have investigated the use of routine health facility data in the more stable, endemic areas of Africa as a low-cost strategy to identify hotspots. Here the objective was to explore the spatial and temporal dynamics of fever positive rapid diagnostic test (RDT) malaria cases routinely collected along the Kenyan Coast. Methods Data on fever positive RDT cases between March 2018 and February 2019 were obtained from patients presenting to six out-patients health-facilities in a rural area of Kilifi County on the Kenyan Coast. To quantify spatial clustering, homestead level geocoded addresses were used as well as aggregated homesteads level data at enumeration zone. Data were sub-divided into quarterly intervals. Kulldorff’s spatial scan statistics using Bernoulli probability model was used to detect hotspots of fever positive RDTs across all ages, where cases were febrile individuals with a positive test and controls were individuals with a negative test. Results Across 12 months of surveillance, there were nine significant clusters that were identified using the spatial scan statistics among RDT positive fevers. These clusters included 52% of all fever positive RDT cases detected in 29% of the geocoded homesteads in the study area. When the resolution of the data was aggregated at enumeration zone (village) level the hotspots identified were located in the same areas. Only two of the nine hotspots were temporally stable accounting for 2.7% of the homesteads and included 10.8% of all fever positive RDT cases detected. Conclusion Taking together the temporal instability of spatial hotspots and the relatively modest fraction of the malaria cases that they account for; it would seem inadvisable to re-design the sub-county control strategies around targeting hotspots.

Abstract Background Understanding the relationship between malaria infection risk and disease outcomes represents a fundamental component of morbidity and mortality burden estimations. Contemporary data on severe malaria risks among populations of different parasite exposures are scarce. Using surveillance data, we compared rates of paediatric malaria hospitalisation in areas of varying parasite exposure levels. Methods Surveillance data at five public hospitals; Jinja, Mubende, Kabale, Tororo, and Apac were assembled among admissions aged 1 month to 14 years between 2017 and 2018. The address of each admission was used to define a local catchment population where national census data was used to define person-year-exposure to risk. Within each catchment, historical infection prevalence was assembled from previously published data and current infection prevalence defined using 33 population-based school surveys among 3400 children. Poisson regression was used to compute the overall and site-specific incidences with 95% confidence intervals. Results Both current and historical Plasmodium falciparum prevalence varied across the five sites. Current prevalence ranged from

Abstract Background Malaria transmission has recently fallen in many parts of Africa, but systematic descriptions of infection and disease across all age groups are rare. Here, an epidemiological investigation of parasite prevalence, the incidence of fevers associated with infection, severe hospitalized disease and mortality among children older than 6 months and adults on the Kenyan coast is presented. Methods A prospective fever surveillance was undertaken at 6 out-patients (OPD) health-facilities between March 2018 and February 2019. Four community-based, cross sectional surveys of fever history and infection prevalence were completed among randomly selected homestead members from the same communities. Paediatric and adult malaria at Kilifi county hospital was obtained for the 12 months period. Rapid Diagnostic Tests (CareStart™ RDT) to detect HRP2-specific to Plasmodium falciparum was used in the community and the OPD, and microscopy in the hospital. Crude and age-specific incidence rates were computed using Poisson regression. Results Parasite prevalence gradually increased from childhood, reaching 12% by 9 years of age then declining through adolescence into adulthood. The incidence rate of RDT positivity in the OPD followed a similar trend to that of infection prevalence in the community. The incidence of hospitalized malaria from the same community was concentrated among children aged 6 months to 4 years (i.e. 64% and 70% of all hospitalized and severe malaria during the 12 months of surveillance, respectively). Only 3.7% (12/316) of deaths were directly attributable to malaria. Malaria mortality was highest among children aged 6 months–4 years at 0.57 per 1000 person-years (95% CI 0.2, 1.2). Severe malaria and death from malaria was negligible above 15 years of age. Conclusion Under conditions of low transmission intensity, immunity to disease and the fatal consequences of infection appear to continue to be acquired in childhood and faster than anti-parasitic immunity. There was no evidence of an emerging significant burden of severe malaria or malaria mortality among adults. This is contrary to current modelled approaches to disease burden estimation in Africa and has important implications for the targeting of infection prevention strategies based on chemoprevention or vector control.

Abstract Background The most widely used measures of declining burden of malaria across sub-Saharan Africa are predictions from geospatial models. These models apply spatiotemporal autocorrelations and covariates to parasite prevalence data and then use a function of parasite prevalence to predict clinical malaria incidence. We attempted to assess whether trends in malaria cases, based on local surveillance, were similar to those captured by Malaria Atlas Project (MAP) incidence surfaces. Methods We undertook a systematic review (PROSPERO International Prospective Register of Systematic Reviews; ID = CRD42019116834) to identify empirical data on clinical malaria in Africa since 2000, where reports covered at least 5 continuous years. The trends in empirical data were then compared with the trends of time-space matched clinical malaria incidence from MAP using the Spearman rank correlation. The correlations (rho) between changes in empirically observed and modelled estimates of clinical malaria were displayed by forest plots and examined by meta-regression. Results Sixty-seven articles met our inclusion criteria representing 124 sites from 24 African countries. The single most important factor explaining the correlation between empirical observations and modelled predictions was the slope of empirically observed data over time (rho = − 0.989; 95% CI − 0.998, − 0.939; p

Abstract Background Use of bednets reduces malaria morbidity and mortality. In Kilifi, Kenya, there was a mass distribution of free nets to children 2500 parasitemia per μl) among children

Abstract Background Malaria transmission intensity is heterogeneous, complicating the implementation of malaria control interventions. We provide a description of the spatial micro-epidemiology of symptomatic malaria and asymptomatic parasitaemia in multiple sites. Methods We assembled data from 19 studies conducted between 1996 and 2015 in seven countries of sub-Saharan Africa with homestead-level geospatial data. Data from each site were used to quantify spatial autocorrelation and examine the temporal stability of hotspots. Parameters from these analyses were examined to identify trends over varying transmission intensity. Results Significant hotspots of malaria transmission were observed in most years and sites. The risk ratios of malaria within hotspots were highest at low malaria positive fractions (MPFs) and decreased with increasing MPF (p