MGSurvE: A framework to optimize trap placement for genetic surveillance of mosquito populations.
Genetic surveillance of mosquito populations is becoming increasingly relevant as genetics-based mosquito control strategies advance from laboratory to field testing. Especially applicable are mosquito gene drive projects, the potential scale of which leads monitoring to be a significant cost driver...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2024-05-01
|
| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1012046&type=printable |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846128237305921536 |
|---|---|
| author | Héctor M Sánchez C David L Smith John M Marshall |
| author_facet | Héctor M Sánchez C David L Smith John M Marshall |
| author_sort | Héctor M Sánchez C |
| collection | DOAJ |
| description | Genetic surveillance of mosquito populations is becoming increasingly relevant as genetics-based mosquito control strategies advance from laboratory to field testing. Especially applicable are mosquito gene drive projects, the potential scale of which leads monitoring to be a significant cost driver. For these projects, monitoring will be required to detect unintended spread of gene drive mosquitoes beyond field sites, and the emergence of alternative alleles, such as drive-resistant alleles or non-functional effector genes, within intervention sites. This entails the need to distribute mosquito traps efficiently such that an allele of interest is detected as quickly as possible-ideally when remediation is still viable. Additionally, insecticide-based tools such as bednets are compromised by insecticide-resistance alleles for which there is also a need to detect as quickly as possible. To this end, we present MGSurvE (Mosquito Gene SurveillancE): a computational framework that optimizes trap placement for genetic surveillance of mosquito populations such that the time to detection of an allele of interest is minimized. A key strength of MGSurvE is that it allows important biological features of mosquitoes and the landscapes they inhabit to be accounted for, namely: i) resources required by mosquitoes (e.g., food sources and aquatic breeding sites) can be explicitly distributed through a landscape, ii) movement of mosquitoes may depend on their sex, the current state of their gonotrophic cycle (if female) and resource attractiveness, and iii) traps may differ in their attractiveness profile. Example MGSurvE analyses are presented to demonstrate optimal trap placement for: i) an Aedes aegypti population in a suburban landscape in Queensland, Australia, and ii) an Anopheles gambiae population on the island of São Tomé, São Tomé and Príncipe. Further documentation and use examples are provided in project's documentation. MGSurvE is intended as a resource for both field and computational researchers interested in mosquito gene surveillance. |
| format | Article |
| id | doaj-art-2e11be34077a4c83b9fb68a0c7b41da2 |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-2e11be34077a4c83b9fb68a0c7b41da22024-12-11T05:31:04ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582024-05-01205e101204610.1371/journal.pcbi.1012046MGSurvE: A framework to optimize trap placement for genetic surveillance of mosquito populations.Héctor M Sánchez CDavid L SmithJohn M MarshallGenetic surveillance of mosquito populations is becoming increasingly relevant as genetics-based mosquito control strategies advance from laboratory to field testing. Especially applicable are mosquito gene drive projects, the potential scale of which leads monitoring to be a significant cost driver. For these projects, monitoring will be required to detect unintended spread of gene drive mosquitoes beyond field sites, and the emergence of alternative alleles, such as drive-resistant alleles or non-functional effector genes, within intervention sites. This entails the need to distribute mosquito traps efficiently such that an allele of interest is detected as quickly as possible-ideally when remediation is still viable. Additionally, insecticide-based tools such as bednets are compromised by insecticide-resistance alleles for which there is also a need to detect as quickly as possible. To this end, we present MGSurvE (Mosquito Gene SurveillancE): a computational framework that optimizes trap placement for genetic surveillance of mosquito populations such that the time to detection of an allele of interest is minimized. A key strength of MGSurvE is that it allows important biological features of mosquitoes and the landscapes they inhabit to be accounted for, namely: i) resources required by mosquitoes (e.g., food sources and aquatic breeding sites) can be explicitly distributed through a landscape, ii) movement of mosquitoes may depend on their sex, the current state of their gonotrophic cycle (if female) and resource attractiveness, and iii) traps may differ in their attractiveness profile. Example MGSurvE analyses are presented to demonstrate optimal trap placement for: i) an Aedes aegypti population in a suburban landscape in Queensland, Australia, and ii) an Anopheles gambiae population on the island of São Tomé, São Tomé and Príncipe. Further documentation and use examples are provided in project's documentation. MGSurvE is intended as a resource for both field and computational researchers interested in mosquito gene surveillance.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1012046&type=printable |
| spellingShingle | Héctor M Sánchez C David L Smith John M Marshall MGSurvE: A framework to optimize trap placement for genetic surveillance of mosquito populations. PLoS Computational Biology |
| title | MGSurvE: A framework to optimize trap placement for genetic surveillance of mosquito populations. |
| title_full | MGSurvE: A framework to optimize trap placement for genetic surveillance of mosquito populations. |
| title_fullStr | MGSurvE: A framework to optimize trap placement for genetic surveillance of mosquito populations. |
| title_full_unstemmed | MGSurvE: A framework to optimize trap placement for genetic surveillance of mosquito populations. |
| title_short | MGSurvE: A framework to optimize trap placement for genetic surveillance of mosquito populations. |
| title_sort | mgsurve a framework to optimize trap placement for genetic surveillance of mosquito populations |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1012046&type=printable |
| work_keys_str_mv | AT hectormsanchezc mgsurveaframeworktooptimizetrapplacementforgeneticsurveillanceofmosquitopopulations AT davidlsmith mgsurveaframeworktooptimizetrapplacementforgeneticsurveillanceofmosquitopopulations AT johnmmarshall mgsurveaframeworktooptimizetrapplacementforgeneticsurveillanceofmosquitopopulations |