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

the final unbiased performance of the optimized model. Each location in the filtered dataset has on average 273 data points, thus resulting in an overall test set size of ~6,500 observations (about 10% of the starting dataset) and a training set of 60,000 observations. We used a grid- search approach to find the best model hyperparameters validated on a further 10% split share of the training set. This validation set was derived by randomly selecting 22 locations for a total of ~6,000 observations, thus leaving 54,000 data points for training the model with the specific parameters under validation. For each parameter, we repeated this procedure 5 times to assure that the resulting validation error score (in terms of symmetric mean absolute per- centage error—SMAPE) would converge. During each reiteration, we re-selected a new ran- dom set of 22 locations from the overall training set as the new validation set. This step led to a measurable improvement in the prediction accuracy over the same algorithm initialized with the default hyperparameters values (base model). Fig 1 shows the performance gain in terms of lower SMAPE error of the optimized model compared to the base model computed on the first randomized test set, which was never seen by both models. On average this optimization procedure results in a ~5–10% lower SMAPE. Table 1 summarizes the set of hyperparameters leading to the best evaluation results. Table 2 reports the training, validation, and test performance of the GBRT model (default and

Fig 1. Accuracy of the based model (left, blue) compared to the optimized model (right, orange) on the test set. We consider only locations with more than 90 data records and records with more than 10 cases per day. https://doi.org/10.1371/journal.pone.0273078.g001 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 8 / 27