Does climate help modeling COVID-19 risk and to what extent?

optimized) compared to two deterministic baselines: the prediction obtained by using a 7-day moving average, and a persistence model, where the value of the predicted dependent variable is assumed to be the same as the previous day. For comparison, we also report the performance of other regression models such as Lasso, Elastic Net, and Random Forest after a cross-vali- dated tuning of their hyperparameters. The optimized GBRT model outperforms all other models on the test set with a mean SMAPE error of 7.5% lower than the base model, 2.4% smaller than the Random Forest model, 11% better than the Lasso model, and 24% lower than the Elastic Net model. High error values for Lasso and Elastic Net are likely related to their lower model complexity that prevents the proper learning of data interrelationships. Random Forest produces a comparable accuracy to xgboost, but it shows overfitting on the train set. Finally, when compared to the two deterministic baselines the tuned xgboost model produces predictions on the test set that are 20.5% and 4% more accurate than the 1-week moving aver- age and persistence models respectively. Fig 2 shows model accuracy distribution as a function of the daily cases grouped in different intervals for all the locations considered in this study. The width of each boxplot is propor- tional to the number of observations included in the specific range (n), which is also reported below the label of each interval. The cross-sectional median error of the model decreases with Table 1. Xgboost hyperparameter tuning result.

Hyperparameter Tuning range Best value Learning rate (eta) 0.001–0.3 0.1 Maximum depth 3–10 10 Minimum sum of instance weight (hessian) 1–10 7–8� Gamma 0–0.4 0.2� Subsample ratio of the training instances 0.5–1 1� Subsample ratio of columns when constructing each tree 0.3–1 1� Lambda 1 1 Alpha 0 0 Number of boosting rounds (validated) ~30 Learning objective function (regression) Squared error Custom evaluation metric (for training and validation) SMAPE Early stopping rounds 10 �Irrelevant to the model performance. https://doi.org/10.1371/journal.pone.0273078.t001 Table 2. Regression modeling performance comparison. Model Mean SMAPE Train Validation Test GBRT (with hyperparameter optimization) 28.3% 30.6% 29.0% GBRT (no optimization) 33.5% - 36.5% Lasso� 45.7% 41.9% 40.1% Elastic Net� 60.2% 60.5% 53.3% Random Forest� 11.9% 31.2% 31.4% 1-week moving average - - 49.5% Persistence (previous day) - - 34.9% �With hyperparameter optimization https://doi.org/10.1371/journal.pone.0273078.t002 PLOS ONE Does climate help modeling COVID-19 risk and to what extent? PLOS ONE | https://doi.org/10.1371/journal.pone.0273078 September 7, 2022 9 / 27