• Alejandro Ruiz-Patiño, Oscar Arrieta, Luis E Pino, Christian Rolfo, Luisa Ricaurte, Gonzalo Recondo, Zyanya-Lucia Zatarain-Barron, Luis Corrales, Claudio Martín, Feliciano Barrón, Carlos Vargas, Hernán Carranza, Jorge Otero, July Rodriguez, Carolina Sotelo, Lucia Viola, Alessandro Russo, Rafael Rosell, and Andrés F Cardona.
• Foundation for Clinical and Applied Cancer Research, Bogotá, Colombia.
• JCO Glob Oncol. 2020 May 1; 6: 752-760.

PurposeIn the midst of a global pandemic, evidence suggests that similar to other severe respiratory viral infections, patients with cancer are at higher risk of becoming infected by COVID-19 and have a poorer prognosis.MethodsWe have modeled the mortality and the intensive care unit (ICU) requirement for the care of patients with cancer infected with COVID-19 in Latin America. A dynamic multistate Markov model was constructed. Transition probabilities were estimated on the basis of published reports for cumulative probability of complications. Basic reproductive number (R0) values were modeled with R using the EpiEstim package. Estimations of days of ICU requirement and absolute mortality were calculated by imputing number of cumulative cases in the Markov model.ResultsEstimated median time of ICU requirement was 12.7 days, median time to mortality was 16.3 days after infection, and median time to severe event was 8.1 days. Peak ICU occupancy for patients with cancer was calculated at 16 days after infection. Deterministic sensitivity analysis revealed an interval for mortality between 18.5% and 30.4%. With the actual incidence tendency, Latin America would be expected to lose approximately 111,725 patients with cancer to SARS-CoV-2 (range, 87,116-143,154 patients) by the 60th day since the start of the outbreak. Losses calculated vary between < 1% to 17.6% of all patients with cancer in the region.ConclusionCancer-related cases and deaths attributable to SARS-CoV-2 will put a great strain on health care systems in Latin America. Early implementation of interventions on the basis of data given by disease modeling could mitigate both infections and deaths among patients with cancer.

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