A Method of Estimating Time-to-Recovery for a Disease Caused by a Contagious Pathogen like SARS-CoV-2 using a Time Series of Aggregated Case Reports

Abstract Background: During the outbreak of a disease caused by a pathogen with unknown characteristics, the uncertainty of its progression parameters can be reduced by devising methods that, based on rational assumptions, exploit available information in order to provide actionable insights. Methods: In this study, performed few (~6) weeks into the outbreak of COVID-19 (caused by SARS-CoV-2), data publicly available on the Internet including daily reported cases of confirmed infections, deaths and recoveries are fed into an algorithm that matches confirmed cases with deaths and recoveries, in order to calculate average time-intervals. Unmatched cases are adjusted based on the matched cases calculation. Results: The mean time-to-recovery calculated from all globally reported cases was found 18.01 days (SD 3.31 days) for the matched cases and 18.29 days (SD 2.73 days), taking under consideration the adjusted unmatched cases as well. Conclusion: The experimental results indicate that the proposed method, in combination with expert knowledge and informed calculated assumptions, could provide a meaningful calculated average time-to-recovery figure, which can be used as an evidence-based estimation to support the containment and mitigation policy decisions. Trial registration: Not applicable.