Impact of COVID-19 on mortality in coastal Kenya: a longitudinal open cohort study.

The mortality impact of COVID-19 in Africa remains controversial because most countries lack vital registration. We analysed excess mortality in Kilifi Health and Demographic Surveillance System, Kenya, using 9 years of baseline data. SARS-CoV-2 seroprevalence studies suggest most adults here were infected before May 2022. During 5 waves of COVID-19 (April 2020-May 2022) an overall excess mortality of 4.8% (95% PI 1.2%, 9.4%) concealed a significant excess (11.6%, 95% PI 5.9%, 18.9%) among older adults ( ≥ 65 years) and a deficit among children aged 1-14 years (-7.7%, 95% PI -20.9%, 6.9%). The excess mortality rate for January 2020-December 2021, age-standardised to the Kenyan population, was 27.4/100,000 person-years (95% CI 23.2-31.6). In Coastal Kenya, excess mortality during the pandemic was substantially lower than in most high-income countries but the significant excess mortality in older adults emphasizes the value of achieving high vaccine coverage in this risk group.

Quantifying previous SARS-CoV-2 infection through mixture modelling of antibody levels.

As countries decide on vaccination strategies and how to ease movement restrictions, estimating the proportion of the population previously infected with SARS-CoV-2 is important for predicting the future burden of COVID-19. This proportion is usually estimated from serosurvey data in two steps: first the proportion above a threshold antibody level is calculated, then the crude estimate is adjusted using external estimates of sensitivity and specificity. A drawback of this approach is that the PCR-confirmed cases used to estimate the sensitivity of the threshold may not be representative of cases in the wider population-e.g., they may be more recently infected and more severely symptomatic. Mixture modelling offers an alternative approach that does not require external data from PCR-confirmed cases. Here we illustrate the bias in the standard threshold-based approach by comparing both approaches using data from several Kenyan serosurveys. We show that the mixture model analysis produces estimates of previous infection that are often substantially higher than the standard threshold analysis.

Three Novel Haplotypes of Theileria bicornis in Black and White Rhinoceros in Kenya.

Piroplasms, especially those in the genera Babesia and Theileria, have been found to naturally infect rhinoceros. Due to natural or human-induced stress factors such as capture and translocations, animals often develop fatal clinical piroplasmosis, which causes death if not treated. This study examines the genetic diversity and occurrence of novel Theileria species infecting both black and white rhinoceros in Kenya. Samples collected opportunistically during routine translocations and clinical interventions from 15 rhinoceros were analysed by polymerase chain reaction (PCR) using a nested amplification of the small subunit ribosomal RNA (18S rRNA) gene fragments of Babesia and Theileria. Our study revealed for the first time in Kenya the presence of Theileria bicornis in white (Ceratotherium simum simum) and black (Diceros bicornis michaeli) rhinoceros and the existence of three new haplotypes: haplotypes H1 and H3 were present in white rhinoceros, while H2 was present in black rhinoceros. No specific haplotype was correlated to any specific geographical location. The Bayesian inference 50% consensus phylogram recovered the three haplotypes monophyleticly, and Theileria bicornis had very high support (BPP: 0.98). Furthermore, the genetic p-uncorrected distances and substitutions between T. bicornis and the three haplotypes were the same in all three haplotypes, indicating a very close genetic affinity. This is the first report of the occurrence of Theileria species in white and black rhinoceros from Kenya. The three new haplotypes reported here for the first time have important ecological and conservational implications, especially for population management and translocation programs and as a means of avoiding the transport of infected animals into non-affected areas.