Evidence map›Paper›PMID 36203397›Full record

ArticleJournal of medical entomology2022

A Process-based Model with Temperature, Water, and Lab-derived Data Improves Predictions of Daily Culex pipiens/restuans Mosquito Density.

D P Shutt, D W Goodsman, K Martinez, Z J L Hemez, J R Conrad, C Xu, D Osthus, C Russell, J M Hyman, C A Manore

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of medical entomology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

D P ShuttInformation Systems and Modeling, Los Alamos National Laboratory, P.O. Box 1663, Los Alamos, NM 87545, USA.
D W GoodsmanEarth and Environmental Sciences, Los Alamos National Laboratory, P.O. Box 1663, Los Alamos, NM 87545, USA.
K MartinezInformation Systems and Modeling, Los Alamos National Laboratory, P.O. Box 1663, Los Alamos, NM 87545, USA.
Z J L HemezComputational Physics Division, Los Alamos National Laboratory, P.O. Box 1663, Los Alamos, NM 87545, USA.
J R ConradInformation Systems and Modeling, Los Alamos National Laboratory, P.O. Box 1663, Los Alamos, NM 87545, USA.
C XuEarth and Environmental Sciences, Los Alamos National Laboratory, P.O. Box 1663, Los Alamos, NM 87545, USA.
D OsthusStatistical Sciences, Los Alamos National Laboratory, P.O. Box 1663, Los Alamos, NM 87545, USA.
C RussellPublic Health Ontario, Canada.
J M HymanDepartment of Mathematics, Tulane University, 6823 St Charles Ave, New Orleans, LA 70118, USA.
C A ManoreEarth and Environmental Sciences, Los Alamos National Laboratory, P.O. Box 1663, Los Alamos, NM 87545, USA.ORCID 0000-0003-2534-9316

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

While the number of human cases of mosquito-borne diseases has increased in North America in the last decade, accurate modeling of mosquito population density has remained a challenge. Longitudinal mosquito trap data over the many years needed for model calibration, and validation is relatively rare. In particular, capturing the relative changes in mosquito abundance across seasons is necessary for predicting the risk of disease spread as it varies from year to year. We developed a discrete, semi-stochastic, mechanistic process-based mosquito population model that captures life-cycle egg, larva, pupa, adult stages, and diapause for Culex pipiens (Diptera, Culicidae) and Culex restuans (Diptera, Culicidae) mosquito populations. This model combines known models for development and survival into a fully connected age-structured model that can reproduce mosquito population dynamics. Mosquito development through these stages is a function of time, temperature, daylight hours, and aquatic habitat availability. The time-dependent parameters are informed by both laboratory studies and mosquito trap data from the Greater Toronto Area. The model incorporates city-wide water-body gauge and precipitation data as a proxy for aquatic habitat. This approach accounts for the nonlinear interaction of temperature and aquatic habitat variability on the mosquito life stages. We demonstrate that the full model predicts the yearly variations in mosquito populations better than a statistical model using the same data sources. This improvement in modeling mosquito abundance can help guide interventions for reducing mosquito abundance in mitigating mosquito-borne diseases like West Nile virus.

Indexed as

CulexCulicidaeWest Nile virusAnimalsHumansPupaTemperatureWaterWaterCulex pipiensmosquitopopulation dynamicsrainfalltemperature

Identifiers

PMID36203397
PMCPMC9667726

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.