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Amplifying and Quantifying HIV-1 RNA in HIV Infected Individuals with Viral Loads Below the Limit of Detection by Standard Clinical Assays

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Weekly Nowcasting of New COVID-19 Cases Using Past Viral Load Measurements.

Athar Khalil^1,2, Khalil Al Handawi^3,4, Zeina Mohsen⁵

¹Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA.

|July 27, 2022

Summary

This study introduces data-driven models using cycle threshold (Ct) and past COVID-19 cases to predict future outbreaks. Polynomial regression and support vector machine regression demonstrated strong generalizability for forecasting COVID-19 incidence across institutions.

Keywords:

COVID-19 Ct values deep neural networks machine learning now-casting predictive modeling statistical analysis viral load

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Area of Science:

Epidemiology
Data Science
Public Health

Background:

COVID-19 presents significant clinical and financial challenges globally.
Accurate forecasting of COVID-19 spread is crucial for resource allocation and policy development.
Existing models often rely on institution-specific data or epidemiological assumptions, leading to prediction uncertainties.

Purpose of the Study:

To develop and validate data-driven models for predicting COVID-19 incidence.
To overcome limitations of previous models by using institution-independent variables: cycle threshold (Ct) and previous case numbers.
To assess the generalizability of machine learning and deep learning algorithms for COVID-19 forecasting.

Main Methods:

Utilized three datasets (n=6296, n=3228, n=12096) for training, validation, and independent validation.
Employed six algorithms: three machine learning (including OLS and SVR) and three deep learning (including sequence-to-sequence).
Evaluated 7-week forward case predictions using Mean Square Error (MSE).

Main Results:

The sequence-to-sequence model achieved the best validation performance (MSE = 0.025).
Polynomial regression (OLS) and Support Vector Machine Regression (SVR) showed superior performance in independent validation (MSE = 0.1596 and 0.16754, respectively), indicating better generalizability.
OLS and SVR models demonstrated promise when applied to external institutional data.

Conclusions:

Data-driven models using Ct and prior case counts can effectively predict COVID-19 spread.
OLS and SVR models exhibit strong generalizability and potential for cross-institutional application.
These predictive models may aid clinical and logistical decision-making in public health responses to COVID-19.