Outbreak Prediction of COVID-19 Patients for Dense and Populated Countries Using Machine Learning

Khakharia, Aman; Shah, Vruddhi; Jain, Sankalp; Shah, Jash; Tiwari, Amanshu; Daphal, Prathamesh; Warang, Mahesh; Mehendale, Ninad

doi:10.2139/ssrn.3655759

Cited by 1 publication

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“…As the pandemic progressed, each state attempted to enhance their data reporting systems. As described by Khakharia et al [41], some regions reported sudden and significant changes in case counts, making it difficult for models to forecast future cases. Though outbreaks are not fundamentally different, the training and test data sets can be characterized separately.…”

Section: Principal Findingsmentioning

confidence: 99%

Evaluation of a Parsimonious COVID-19 Outbreak Prediction Model: Heuristic Modeling Approach Using Publicly Available Data Sets

Gupta¹,

Grannis²,

Kasthurirathne³

2021

J Med Internet Res

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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.

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Section: Principal Findingsmentioning

confidence: 99%