COVID Border Accountability Project, a hand-coded global database of border closures introduced during 2020.

Quantifying the timing and content of policy changes affecting international travel and immigration is key to ongoing research on the spread of SARS-CoV-2 and the socioeconomic impacts of border closures. The COVID Border Accountability Project (COBAP) provides a hand-coded dataset of >1000 policies systematized to reflect a complete timeline of country-level restrictions on movement across international borders during 2020. Trained research assistants used pre-set definitions to source, categorize and verify for each new border policy: start and end dates, whether the closure is "complete" or "partial", which exceptions are made, which countries are banned, and which air/land/sea borders were closed. COBAP verified the database through internal and external audits from public health experts. For purposes of further verification and future data mining efforts of pandemic research, the full text of each policy was archived. The structure of the COBAP dataset is designed for use by social and biomedical scientists. For broad accessibility to policymakers and the public, our website depicts the data in an interactive, user-friendly, time-based map.

Integrating additional factors into the TNM staging for cutaneous melanoma by machine learning.

BACKGROUND: Integrating additional factors into the TNM staging system is needed for more accurate risk classification and survival prediction for patients with cutaneous melanoma. In the present study, we introduce machine learning as a novel tool that incorporates additional prognostic factors to improve the current TNM staging system. METHODS AND FINDINGS: Cancer-specific survival data for cutaneous melanoma with at least a 5 years follow-up were extracted from the Surveillance, Epidemiology, and End Results Program of the National Cancer Institute and split into the training set (40,781 cases) and validation set (5,390 cases). Five factors were studied: the primary tumor (T), regional lymph nodes (N), distant metastasis (M), age (A), and sex (S). The Ensemble Algorithm for Clustering Cancer Data (EACCD) was applied to the training set to generate prognostic groups. Utilizing only T, N, and M, a basic prognostic system was built where patients were stratified into 10 prognostic groups with well-separated survival curves, similar to 10 AJCC stages. These 10 groups had a significantly higher accuracy in survival prediction than 10 stages (C-index = 0.7682 vs 0.7643; increase in C-index = 0.0039, 95% CI = (0.0032, 0.0047); p-value = 7.2×10-23). Nevertheless, a positive association remained between the EACCD grouping and the AJCC staging (Spearman's rank correlation coefficient = 0.8316; p-value = 4.5×10-13). With additional information from A and S, a more advanced prognostic system was established using the training data that stratified patients into 10 groups and further improved the prediction accuracy (C-index = 0.7865 vs 0.7643; increase in C-index = 0.0222, 95% CI = (0.0191, 0.0254); p-value = 8.8×10-43). Both internal validation using the training set and temporal validation using the validation set showed good stratification and a high predictive accuracy of the prognostic systems. CONCLUSIONS: The EACCD allows additional factors to be integrated into the TNM to create a prognostic system that improves patient stratification and survival prediction for cutaneous melanoma. This integration separates favorable from unfavorable clinical outcomes for patients and improves both cohort selection for clinical trials and treatment management.