Your browser doesn't support javascript.
Evaluation of a Parsimonious COVID-19 Outbreak Prediction Model: Heuristic Modeling Approach Using Publicly Available Data Sets.
Gupta, Agrayan K; Grannis, Shaun J; Kasthurirathne, Suranga N.
  • Gupta AK; Indiana University, Bloomington, IN, United States.
  • Grannis SJ; Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, IN, United States.
  • Kasthurirathne SN; Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, IN, United States.
J Med Internet Res ; 23(7): e28812, 2021 07 26.
Article in English | MEDLINE | ID: covidwho-1334873
ABSTRACT

BACKGROUND:

The COVID-19 pandemic has changed public health policies and human and community behaviors through lockdowns and mandates. Governments are rapidly evolving policies to increase hospital capacity and supply personal protective equipment and other equipment to mitigate disease spread in affected regions. Current models that predict COVID-19 case counts and spread are complex by nature and offer limited explainability and generalizability. This has highlighted the need for accurate and robust outbreak prediction models that balance model parsimony and performance.

OBJECTIVE:

We sought to leverage readily accessible data sets extracted from multiple states to train and evaluate a parsimonious predictive model capable of identifying county-level risk of COVID-19 outbreaks on a day-to-day basis.

METHODS:

Our modeling approach leveraged the following data inputs COVID-19 case counts per county per day and county populations. We developed an outbreak gold standard across California, Indiana, and Iowa. The model utilized a per capita running 7-day sum of the case counts per county per day and the mean cumulative case count to develop baseline values. The model was trained with data recorded between March 1 and August 31, 2020, and tested on data recorded between September 1 and October 31, 2020.

RESULTS:

The model reported sensitivities of 81%, 92%, and 90% for California, Indiana, and Iowa, respectively. The precision in each state was above 85% while specificity and accuracy scores were generally >95%.

CONCLUSIONS:

Our parsimonious model provides a generalizable and simple alternative approach to outbreak prediction. This methodology can be applied to diverse regions to help state officials and hospitals with resource allocation and to guide risk management, community education, and mitigation strategies.
Subject(s)
Keywords

Full text: Available Collection: International databases Database: MEDLINE Main subject: Computer Simulation / Disease Outbreaks / Public Sector / Datasets as Topic / Heuristics / Forecasting / COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study Limits: Humans Country/Region as subject: North America Language: English Journal: J Med Internet Res Journal subject: Medical Informatics Year: 2021 Document Type: Article Affiliation country: 28812

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: Computer Simulation / Disease Outbreaks / Public Sector / Datasets as Topic / Heuristics / Forecasting / COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study Limits: Humans Country/Region as subject: North America Language: English Journal: J Med Internet Res Journal subject: Medical Informatics Year: 2021 Document Type: Article Affiliation country: 28812