Quantifying uncertainty about future antimicrobial resistance: comparing structured expert judgement and statistical forecasting methods

Research output: Contribution to conferenceAbstract

Abstract

The increase in antibiotic resistance is a defining development in the treatment of infectious diseases in this century. In response, the international community is investing in programs to protect access to effective antibiotics. Information is needed on the uncertainty surrounding the future trajectory of resistance. Mathematical and statistical models capable of projecting future rates are challenged by the paucity of data and the complexity of the emergence and spread of resistance, but experts have relevant knowledge. We use the Classical Model of structured expert judgment to elicit projections with uncertainty bounds of resistance rates through 2026 for nine pathogen-antibiotic pairs in four European countries and empirically validate the assessments against data on a set of calibration questions. The performance-weighted combination of experts projects rates of resistance in 2026 will range from 1% for carbapenem-resistant Escherichia coli in France (90% credible range: 0.10% to 8%) to 50% for third-generation cephalosporin-resistant Klebsiella pneumoniae in Italy (90% credible range: 40% to 70%). We use historical data from the European Antimicrobial Resistance Surveillance Network (EARS-Net) to develop statistical forecasts with exponential smoothing and autoregressive integrated moving average (ARIMA) models. Results from the statistical models differ from each other and from the judgmental forecasts in many cases. The judgmental forecasts include information from the experts about the impact of current and future shifts in infection control, antibiotic usage, and other factors that cannot be easily captured in statistical forecasts. Structured expert judgment is a useful tool for better understanding uncertainty about future antibiotic resistance.

Conference

ConferenceScenario Planning and Foresight 2018
Abbreviated titleScenario 2018
CountryUnited Kingdom
CityCoventry
Period10/12/1811/12/18
Internet address

Fingerprint

Uncertainty
Statistical Models
Microbial Drug Resistance
Anti-Bacterial Agents
Carbapenems
Klebsiella pneumoniae
Cephalosporins
Infection Control
Italy
Calibration
France
Communicable Diseases
Theoretical Models
Escherichia coli
Forecasting method
Antimicrobial resistance
Expert judgment
Antibiotics
Antibiotic resistance
Statistical model

Keywords

  • antibiotic resistance
  • international community
  • statistical forecasting methods
  • structured expert judgement

Cite this

@conference{90d9b352e6bc40958b4249cc9cff2c56,
title = "Quantifying uncertainty about future antimicrobial resistance: comparing structured expert judgement and statistical forecasting methods",
abstract = "The increase in antibiotic resistance is a defining development in the treatment of infectious diseases in this century. In response, the international community is investing in programs to protect access to effective antibiotics. Information is needed on the uncertainty surrounding the future trajectory of resistance. Mathematical and statistical models capable of projecting future rates are challenged by the paucity of data and the complexity of the emergence and spread of resistance, but experts have relevant knowledge. We use the Classical Model of structured expert judgment to elicit projections with uncertainty bounds of resistance rates through 2026 for nine pathogen-antibiotic pairs in four European countries and empirically validate the assessments against data on a set of calibration questions. The performance-weighted combination of experts projects rates of resistance in 2026 will range from 1{\%} for carbapenem-resistant Escherichia coli in France (90{\%} credible range: 0.10{\%} to 8{\%}) to 50{\%} for third-generation cephalosporin-resistant Klebsiella pneumoniae in Italy (90{\%} credible range: 40{\%} to 70{\%}). We use historical data from the European Antimicrobial Resistance Surveillance Network (EARS-Net) to develop statistical forecasts with exponential smoothing and autoregressive integrated moving average (ARIMA) models. Results from the statistical models differ from each other and from the judgmental forecasts in many cases. The judgmental forecasts include information from the experts about the impact of current and future shifts in infection control, antibiotic usage, and other factors that cannot be easily captured in statistical forecasts. Structured expert judgment is a useful tool for better understanding uncertainty about future antibiotic resistance.",
keywords = "antibiotic resistance, international community, statistical forecasting methods, structured expert judgement",
author = "Abigail Colson",
year = "2018",
month = "12",
day = "10",
language = "English",
note = "Scenario Planning and Foresight 2018, Scenario 2018 ; Conference date: 10-12-2018 Through 11-12-2018",
url = "https://warwick.ac.uk/fac/soc/wbs/subjects/orms/ormsevents/scenario2018/",

}

Quantifying uncertainty about future antimicrobial resistance : comparing structured expert judgement and statistical forecasting methods. / Colson, Abigail.

2018. Abstract from Scenario Planning and Foresight 2018, Coventry, United Kingdom.

Research output: Contribution to conferenceAbstract

TY - CONF

T1 - Quantifying uncertainty about future antimicrobial resistance

T2 - comparing structured expert judgement and statistical forecasting methods

AU - Colson, Abigail

PY - 2018/12/10

Y1 - 2018/12/10

N2 - The increase in antibiotic resistance is a defining development in the treatment of infectious diseases in this century. In response, the international community is investing in programs to protect access to effective antibiotics. Information is needed on the uncertainty surrounding the future trajectory of resistance. Mathematical and statistical models capable of projecting future rates are challenged by the paucity of data and the complexity of the emergence and spread of resistance, but experts have relevant knowledge. We use the Classical Model of structured expert judgment to elicit projections with uncertainty bounds of resistance rates through 2026 for nine pathogen-antibiotic pairs in four European countries and empirically validate the assessments against data on a set of calibration questions. The performance-weighted combination of experts projects rates of resistance in 2026 will range from 1% for carbapenem-resistant Escherichia coli in France (90% credible range: 0.10% to 8%) to 50% for third-generation cephalosporin-resistant Klebsiella pneumoniae in Italy (90% credible range: 40% to 70%). We use historical data from the European Antimicrobial Resistance Surveillance Network (EARS-Net) to develop statistical forecasts with exponential smoothing and autoregressive integrated moving average (ARIMA) models. Results from the statistical models differ from each other and from the judgmental forecasts in many cases. The judgmental forecasts include information from the experts about the impact of current and future shifts in infection control, antibiotic usage, and other factors that cannot be easily captured in statistical forecasts. Structured expert judgment is a useful tool for better understanding uncertainty about future antibiotic resistance.

AB - The increase in antibiotic resistance is a defining development in the treatment of infectious diseases in this century. In response, the international community is investing in programs to protect access to effective antibiotics. Information is needed on the uncertainty surrounding the future trajectory of resistance. Mathematical and statistical models capable of projecting future rates are challenged by the paucity of data and the complexity of the emergence and spread of resistance, but experts have relevant knowledge. We use the Classical Model of structured expert judgment to elicit projections with uncertainty bounds of resistance rates through 2026 for nine pathogen-antibiotic pairs in four European countries and empirically validate the assessments against data on a set of calibration questions. The performance-weighted combination of experts projects rates of resistance in 2026 will range from 1% for carbapenem-resistant Escherichia coli in France (90% credible range: 0.10% to 8%) to 50% for third-generation cephalosporin-resistant Klebsiella pneumoniae in Italy (90% credible range: 40% to 70%). We use historical data from the European Antimicrobial Resistance Surveillance Network (EARS-Net) to develop statistical forecasts with exponential smoothing and autoregressive integrated moving average (ARIMA) models. Results from the statistical models differ from each other and from the judgmental forecasts in many cases. The judgmental forecasts include information from the experts about the impact of current and future shifts in infection control, antibiotic usage, and other factors that cannot be easily captured in statistical forecasts. Structured expert judgment is a useful tool for better understanding uncertainty about future antibiotic resistance.

KW - antibiotic resistance

KW - international community

KW - statistical forecasting methods

KW - structured expert judgement

UR - https://warwick.ac.uk/fac/soc/wbs/subjects/orms/ormsevents/scenario2018/

M3 - Abstract

ER -