Evidence map›Paper›PMID 40269898›Full record

ArticleParasites & vectors2025

Evaluation of the 2022 West Nile virus forecasting challenge, USA.

Ryan D Harp, Karen M Holcomb, Renata Retkute, Alisa Prusokiene, Augustinas Prusokas, Zeynep Ertem, Marco Ajelli, Allisandra G Kummer, Maria Litvinova, Stefano Merler and 27 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Parasites & vectors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

37 authors.

Ryan D HarpDivision of Vector-Borne Diseases, Centers for Disease Control and Prevention, Fort Collins, CO, USA. ryan.harp@noaa.gov.
Karen M HolcombDivision of Vector-Borne Diseases, Centers for Disease Control and Prevention, Fort Collins, CO, USA.
Renata RetkuteDepartment of Plant Sciences, University of Cambridge, Cambridge, UK.
Alisa PrusokieneSchool of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, UK.
Augustinas PrusokasIndependent Researcher, London, UK.
Zeynep ErtemSchool of Systems Science and Industrial Engineering, Binghamton University, State University of New York, Binghamton, NY, USA.
Marco AjelliLaboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA.
Allisandra G KummerLaboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA.
Maria LitvinovaDepartment of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA.
Stefano MerlerCenter for Health Emergencies, Fondazione Bruno Kessler, Trento, Italy.
Ana Pastore Y PionttiLaboratory for the Modeling of Biological and Socio-Technical Systems, Network Science Institute, Northeastern University, Boston, MA, USA.
Piero PolettiCenter for Health Emergencies, Fondazione Bruno Kessler, Trento, Italy.
Alessandro VespignaniLaboratory for the Modeling of Biological and Socio-Technical Systems, Network Science Institute, Northeastern University, Boston, MA, USA.
Andre B B WilkeLaboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA.
Agnese ZardiniCenter for Health Emergencies, Fondazione Bruno Kessler, Trento, Italy.
Kelly Helm SmithNational Drought Mitigation Center, University of Nebraska-Lincoln, Lincoln, NE, USA.
Philip ArmstrongConnecticut Agricultural Experiment Station, New Haven, CT, USA.
Nicholas DeFeliceIcahn School of Medicine at Mount Sinai, New York, NY, USA.
Alexander KeyelDivision of Infectious Diseases, Wadsworth Center, New York State Department of Health, Albany, NY, USA.
John ShepardConnecticut Agricultural Experiment Station, New Haven, CT, USA.
Rebecca SmithDepartment of Pathobiology, University of Illinois at Urbana-Champaign, Champaign, IL, USA.
Andrew TyreRegulatory Science, Bayer AG, Chesterfield, MO, USA.
John HumphreysForeign Animal Diseases Research Unit, Agricultural Research Service, National Bio- and Agro-Defense Facility, U.S. Department of Agriculture, Manhattan, KS, USA.
Lee W CohnstaedtForeign Arthropod-Borne Animal Diseases Research Unit, Agricultural Research Service, National Bio- and Agro-Defense Facility, U.S. Department of Agriculture, Manhattan, KS, USA.
Saman HosseiniDepartment of Electrical and Computer Engineering, Kansas State University, Manhattan, KS, USA.
Caterina ScoglioDepartment of Electrical and Computer Engineering, Kansas State University, Manhattan, KS, USA.
Morgan E GorrisInformation Systems and Modeling, Los Alamos National Laboratory, Los Alamos, NM, USA.
Martha BarnardInformation Systems and Modeling, Los Alamos National Laboratory, Los Alamos, NM, USA.
S Kane MoserGenomics and Bioanalytics, Los Alamos National Laboratory, Los Alamos, NM, USA.
Julie A SpencerInformation Systems and Modeling, Los Alamos National Laboratory, Los Alamos, NM, USA.
Maggie S J McCarterDepartment of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, USA.
Christopher LeeDepartment of Computer Science and Engineering, University of South Carolina, Columbia, SC, USA.
Melissa S NolanDepartment of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, USA.
Christopher M BarkerDepartment of Pathology, Microbiology, and Immunology, School of Veterinary Medicine, University of California, Davis, CA, USA.
J Erin StaplesDivision of Vector-Borne Diseases, Centers for Disease Control and Prevention, Fort Collins, CO, USA.
Randall J NettDivision of Vector-Borne Diseases, Centers for Disease Control and Prevention, Fort Collins, CO, USA.
Michael A JohanssonDivision of Vector-Borne Diseases, Centers for Disease Control and Prevention, San Juan, PR, USA.

Funding

Modeling the influence of temperature on the evolution of vector-virus interactionsR01AI168097 · NIAID · WADSWORTH CENTER · PI Alexander Timothy Ciota · 2022 to 2026
$2.4M
Pandemic preparedness in schools: A community based approach for sentinel surveillanceK25HD109509 · NICHD · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Nicholas B DeFelice · 2022 to 2026
$656k
Los Alamos National Laboratory 20200682PRD1National Institute of Child Health and Human Development K25 HD109509-01National Oceanic and Atmospheric Administration NA21OAR4310473NIAID NIH HHS R01 AI168097NICHD NIH HHS K25 HD109509NIEHS NIH HHS P30ES02351NIH HHS R01AI168097
6 · The paper itself

Abstract

backgroundWest Nile virus (WNV) is the most common cause of mosquito-borne disease in the continental USA, with an average of ~1200 severe, neuroinvasive cases reported annually from 2005 to 2021 (range 386-2873). Despite this burden, efforts to forecast WNV disease to inform public health measures to reduce disease incidence have had limited success. Here, we analyze forecasts submitted to the 2022 WNV Forecasting Challenge, a follow-up to the 2020 WNV Forecasting Challenge.

methodsForecasting teams submitted probabilistic forecasts of annual West Nile virus neuroinvasive disease (WNND) cases for each county in the continental USA for the 2022 WNV season. We assessed the skill of team-specific forecasts, baseline forecasts, and an ensemble created from team-specific forecasts. We then characterized the impact of model characteristics and county-specific contextual factors (e.g., population) on forecast skill.

resultsEnsemble forecasts for 2022 anticipated a season at or below median long-term WNND incidence for nearly all (> 99%) counties. More counties reported higher case numbers than anticipated by the ensemble forecast median, but national caseload (826) was well below the 10-year median (1386). Forecast skill was highest for the ensemble forecast, though the historical negative binomial baseline model and several team-submitted forecasts had similar forecast skill. Forecasts utilizing regression-based frameworks tended to have more skill than those that did not and models using climate, mosquito surveillance, demographic, or avian data had less skill than those that did not, potentially due to overfitting. County-contextual analysis showed strong relationships with the number of years that WNND had been reported and permutation entropy (historical variability). Evaluations based on weighted interval score and logarithmic scoring metrics produced similar results.

conclusionsThe relative success of the ensemble forecast, the best forecast for 2022, suggests potential gains in community ability to forecast WNV, an improvement from the 2020 Challenge. Similar to the previous challenge, however, our results indicate that skill was still limited with general underprediction despite a relative low incidence year. Potential opportunities for improvement include refining mechanistic approaches, integrating additional data sources, and considering different approaches for areas with and without previous cases.

Indexed as

West Nile FeverWest Nile virusAnimalsCulicidaeForecastingHumansIncidenceMosquito VectorsSeasonsUnited StatesEnsembleForecastingLogarithmic scoringMulti-model assessmentVector-borne diseaseWeighted interval scoringWest Nile virusWest Nile virus neuroinvasive disease

Identifiers

PMID40269898
PMCPMC12020065

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.