Evidence map›Paper›PMID 40971405›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2025

Strategic planning of prevention and surveillance for emerging diseases and invasive species.

Jue Wang, Brenda J Hanley, Noelle E Thompson, Yu Gong, Daniel P Walsh, Carlos Gonzalez-Crespo, Yitong Huang, James G Booth, Joe N Caudell, Landon A Miller and 1 more

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Choosing structure over complexity: POMDPs for emerging diseases and invasive species.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Jue WangSmith School of Business, Queen's University, Kingston, ON K7L 3N6, Canada.ORCID 0000-0002-4162-8852
Brenda J HanleyWildlife Health Lab, Animal Health Diagnostic Center, Department of Public and Ecosystem Health, Cornell University, Ithaca, NY 14850.ORCID 0000-0002-0380-6267
Noelle E ThompsonWestern Association of Fish and Wildlife Agencies, Boise, ID 83719.
Yu GongSmith School of Business, Queen's University, Kingston, ON K7L 3N6, Canada.ORCID 0009-0009-6761-1029
Daniel P WalshU.S. Geological Survey, Montana Cooperative Wildlife Research Unit,Wildlife Biology Program, University of Montana, Missoula, MT 59812.ORCID 0000-0002-7772-2445
Carlos Gonzalez-CrespoCenter for Animal Disease Modeling and Surveillance, School of Veterinary Medicine, University of California, Davis, CA 95616.
Yitong HuangCenter for Animal Disease Modeling and Surveillance, School of Veterinary Medicine, University of California, Davis, CA 95616.ORCID 0009-0004-1398-1888
James G BoothDepartment of Statistics and Data Science, Cornell University, Ithaca, NY 14853.
Joe N CaudellOffice of Science and Research, Indiana Department of Natural Resources, Bloomington, IN 47401.
Landon A MillerWildlife Health Unit, New York State Department of Environmental Conservation, Delmar, NY 12054.
Krysten L SchulerWildlife Health Lab, Animal Health Diagnostic Center, Department of Public and Ecosystem Health, Cornell University, Ithaca, NY 14850.ORCID 0000-0002-0515-3589

Funding

Canadian Government | Natural Sciences and Engineering Research Council of Canada (NSERC) GPIN-2019-05671Federal Aid Wildlife Restoration Grant W-178-RMultistate Conservation Grant F23AP00488-00US Department of Agriculture AP24WSNWRC00C030
6 · The paper itself

Abstract

Emerging infectious diseases and biological invasions pose increasing threats to public and ecosystems health. Proactive measures-such as prevention and surveillance taken before initial detection of the pathogen or species-are essential to ensure minimal spread prior to first detection. We developed an optimization model to determine where, when, and how much effort should be allocated to prevention versus surveillance. The model accounts for imperfect detection, system dynamics, spatial heterogeneity in risk and costs and is scalable to large landscapes. We found that the most cost-effective strategy is to maintain the prevention and surveillance efforts at stable equilibrium for the majority of the time, with deviations occurring only initially to steer the system toward the equilibrium. The equilibrium effort is jointly determined by the introduction risk, management costs, and total budget. Application of this model to chronic wasting disease in New York State suggests that the optimal strategy could reduce the cumulative disease cases before initial detection by an average of 22% compared to current practice. The optimal surveillance strategy could detect the disease on average over 8 mo earlier than the current strategy.

Indexed as

Communicable Diseases, EmergingIntroduced SpeciesStrategic PlanningAnimalsHumansNew YorkPopulation Surveillanceemerging infectious diseaseinvasive speciespartially observable Markov decision processes (POMDP)preventionsurveillance

Identifiers

PMID40971405
PMCPMC12501194

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.