Biology subjects

Okumu, F.

Publications and source records attributed to Okumu, F..

3 recordsLinked to original sources

bioRxiv2026

TAUS: Target-Age Unified Survival. Survival analysis without assuming proportional hazards or parameterising the survival function.

O_LIStandard survival analysis methods often rely on the assumption of proportional hazards (PH) or parameterisations of the survival function that might not be appropriate for wild populations. C_LIO_LITo enable survival analysis without these modelling constraints, we developed an approach that combines the Kaplan-Meier estimator with conditional probability theory to compute age-specific probabilities of survival up to some target age of choice{tau} . Marginalising this probability over the age distribution of the population yields O{tau}, the probability that a randomly sampled individual of unknown age will outlive the target age{tau} . Notably, the value for{tau} is set by the analyst for each group independently, which allows accounting for differences in pace of life across populations. C_LIO_LIWe tested its application using a simulation study and two real-world datasets, and compared its performance against that of Cox PH and parametric survival models. The PH assumption was violated in the three examples, rendering the Cox PH models inappropriate. Parametric models offered a better alternative, but the best parametric fit missed at least some key survival patterns in all examples. The TAUS model provided a valid description of survival patterns in all cases. Its richer output also allowed finer analysis of survival differences between populations. C_LIO_LIThe TAUS model is also available as an R package (https://github.com/casasgomezuribarri/TAUS). This new approach to survival analysis without PH or parametric assumptions allows the comparison of survival probabilities across populations with different age structures and rates of pace of life. This makes it suitable for a wide range of ecological applications, including in population viability analysis, epidemiology, or life-history theory C_LI

ecology↗

bioRxiv2024

Genomic diversity of the African malaria vector Anopheles funestus

Anopheles funestus s.s. is a formidable human malaria vector across sub-Saharan Africa. To understand how the species is evolving, especially in response to malaria vector control, we sequenced 656 modern specimens (collected 2014-2018) and 45 historic specimens (collected 1927-1967) from 16 African countries. We find high levels of genetic variation with clear and stable continental patterns. Six segregating inversions might be involved in adaptation of local ecotypes. Strong recent signals of selection centred on canonical insecticide resistance genes are shared by multiple populations. A promising gene drive target in An. gambiae is highly conserved in An. funestus. This work represents a significant advance in our understanding of the genetic diversity and population structure of An. funestus and will enable smarter targeted malaria control.

genomics↗

bioRxiv2020

An Autoencoder and Artificial Neural Network-based Method to Estimate Parity Status of Wild Mosquitoes from Near-infrared Spectra

BackgroundAfter mating, female mosquitoes need animal blood to develop their eggs. In the process of acquiring blood, they may acquire pathogens, which may cause different diseases to humans such as malaria, zika, dengue, and chikungunya. Therefore, knowing the parity status of mosquitoes is useful in control and evaluation of infectious diseases transmitted by mosquitoes, where parous mosquitoes are assumed to be potentially infectious. Ovary dissections, which currently are used to determine the parity status of mosquitoes, are very tedious and limited to very few experts. An alternative to ovary dissections is near-infrared spectroscopy (NIRS), which can estimate the age in days and the infectious state of laboratory and semi-field reared mosquitoes with accuracies between 80 and 99%. No study has tested the accuracy of NIRS for estimating the parity status of wild mosquitoes. Methods and resultsIn this study, we train artificial neural network (ANN) models on NIR spectra to estimate the parity status of wild mosquitoes. We use four different datasets: An. arabiensis collected from Minepa, Tanzania (Minepa-ARA); An. gambiae collected from Muleba, Tanzania (Muleba-GA); An. gambiae collected from Burkina Faso (Burkina-GA); and An.gambiae from Muleba and Burkina Faso combined (Muleba-Burkina-GA). We train ANN models on datasets with spectra preprocessed according to previous protocols. We then use autoencoders to reduce the spectra feature dimensions from 1851 to 10 and re-train ANN models. Before the autoencoder was applied, ANN models estimated parity status of mosquitoes in Minepa-ARA, Muleba-GA, Burkina-GA and Muleba-Burkina-GA with out-of-sample accuracies of 81.9 {+/-} 2.8% (N=927), 68.7 {+/-} 4.8% (N=140), 80.3 {+/-} 2.0% (N=158), and 75.7 {+/-} 2.5% (N=298), respectively. With the autoencoder, ANN models tested on out-of-sample data achieved 97.1 {+/-} 2.2%, (N=927), 89.8 {+/-} 1.7% (N=140), 93.3 {+/-} 1.2% (N=158), and 92.7 {+/-} 1.8% (N=298) accuracies for Minepa-ARA, Muleba-GA, Burkina-GA, and Muleba-Burkina-GA, respectively. ConclusionThese results show that a combination of an autoencoder and an ANN trained on NIR spectra to estimate parity status of wild mosquitoes yields models that can be used as an alternative tool to estimate parity status of wild mosquitoes, especially since NIRS is a high-throughput, reagent-free, and simple-to-use technique compared to ovary dissections.

bioinformatics↗