Handling Missing Data in COVID-19 Incidence Estimation: Secondary Data Analysis.

ABSTRACT:

Background

The COVID-19 pandemic has revealed significant challenges in disease forecasting and in developing a public health response, emphasizing the need to manage missing data from various sources in making accurate forecasts.

Objective

We aimed to show how handling missing data can affect estimates of the COVID-19 incidence rate (CIR) in different pandemic situations.

Methods

This study used data from the COVID-19/SARS-CoV-2 surveillance system at the National Institute of Hygiene and Epidemiology, Vietnam. We separated the available data set into 3 distinct periods: zero COVID-19, transition, and new normal. We randomly removed 5% to 30% of data that were missing completely at random, with a break of 5% at each time point in the variable daily caseload of COVID-

SUBMITTER: Pham HT 

PROVIDER: S-EPMC11350390 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature