A dynamic model for estimating adult female mortality from ovarian dissection data for the tsetse fly Glossina pallidipes Austen sampled in Zimbabwe
Bibliographic record
Abstract
Human and animal trypanosomiasis, spread by tsetse flies (Glossina spp), is a major public health concern in much of sub-Saharan Africa. The basic reproduction number of vector-borne diseases, such as trypanosomiasis, is a function of vector mortality rate. Robust methods for estimating tsetse mortality are thus of interest for understanding population and disease dynamics and for optimal control. Existing methods for estimating mortality in adult tsetse, from ovarian dissection data, often use invalid assumptions of the existence of a stable age distribution, and age-invariant mortality and capture probability. We develop a dynamic model to estimate tsetse mortality from ovarian dissection data in populations where the age distribution is not necessarily stable. The models correspond to several hypotheses about how temperature affects mortality: no temperature dependence (model 1), identical temperature dependence for mature adults and immature stages, i.e., pupae and newly emerged adults (model 2), and differential temperature dependence for mature adults and immature stages (model 3). We fit our models to ovarian dissection data for G. pallidipes collected at Rekomitjie Research Station in the Zambezi Valley in Zimbabwe. We compare model fits to determine the most probable model, given the data, by calculating the Akaike Information Criterion (AIC) for each model. The model that allows for a differential dependence of temperature on mortality for immature stages and mature adults (model 3) performs significantly better than models 1 and 2. All models produce mortality estimates, for mature adults, of approximately 3% per day for mean daily temperatures below 25°C, consistent with those of mark-recapture studies performed in other settings. For temperatures greater than 25°C, mortality among immature classes of tsetse increases substantially, whereas mortality remains roughly constant for mature adults. As a sensitivity analysis, model 3 was simultaneously fit to both the ovarian dissection and trap data; while this fit also produces comparable mortality at temperatures below 25°C, it is not possible to obtain good fits to both data sources simultaneously, highlighting the uncertain correspondence between trap catches and population levels and/or the need for further improvements to our model. The modelling approach employed here could be applied to any substantial time series of age distribution data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".