Optimization of Infectious Disease Prevention and Control Policies Using Artificial Life

Handawi, Khalil Al; Kokkolaras, Michael

doi:10.1109/tetci.2021.3107496

Cited by 4 publications

(1 citation statement)

References 45 publications

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“…However, it does not provide guidance on what measures could best control an upcoming peak. Mechanistic models, on the other hand, such as individualbased models (IBMs), can provide such insights but their application is limited to a much smaller population size due to computational cost [ 52 , 53 , 54 , 55 ]. A future study could focus on combining IBMs with viral load models such as those developed by Hay et al [ 24 ] to estimate Ct values for a cross-section of the population and use them to retrain the models developed in this paper to now-cast the trajectory under different intervention policies [ 24 ].…”

Section: Discussionmentioning

confidence: 99%

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

Khalil

Handawi

Mohsen

et al. 2022

Viruses

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The rapid spread of the coronavirus disease COVID-19 has imposed clinical and financial burdens on hospitals and governments attempting to provide patients with medical care and implement disease-controlling policies. The transmissibility of the disease was shown to be correlated with the patient’s viral load, which can be measured during testing using the cycle threshold (Ct). Previous models have utilized Ct to forecast the trajectory of the spread, which can provide valuable information to better allocate resources and change policies. However, these models combined other variables specific to medical institutions or came in the form of compartmental models that rely on epidemiological assumptions, all of which could impose prediction uncertainties. In this study, we overcome these limitations using data-driven modeling that utilizes Ct and previous number of cases, two institution-independent variables. We collected three groups of patients (n = 6296, n = 3228, and n = 12,096) from different time periods to train, validate, and independently validate the models. We used three machine learning algorithms and three deep learning algorithms that can model the temporal dynamic behavior of the number of cases. The endpoint was 7-week forward number of cases, and the prediction was evaluated using mean square error (MSE). The sequence-to-sequence model showed the best prediction during validation (MSE = 0.025), while polynomial regression (OLS) and support vector machine regression (SVR) had better performance during independent validation (MSE = 0.1596, and MSE = 0.16754, respectively), which exhibited better generalizability of the latter. The OLS and SVR models were used on a dataset from an external institution and showed promise in predicting COVID-19 incidences across institutions. These models may support clinical and logistic decision-making after prospective validation.

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