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A hypothesis-free bridging of disease dynamics and non-pharmaceutical policies

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dc.contributor.authorXiunan Wang
dc.contributor.authorHao Wang
dc.contributor.authorPouria Ramazi
dc.contributor.authorKyeongah Nah
dc.contributor.authorMark Lewis
dc.date.accessioned2025-05-01T12:01:03Z
dc.date.available2025-05-01T12:01:03Z
dc.date.issued2022-03-10
dc.descriptionAccurate prediction of the number of daily or weekly confirmed cases of COVID-19 is critical to the control of the pandemic. Existing mechanistic models nicely capture the disease dynamics. However, to forecast the future, they require the transmission rate to be known, limiting their prediction power. Typically, a hypothesis is made on the form of the transmission rate with respect to time. Yet the real form is too complex to be mechanistically modeled due to the unknown dynamics of many influential factors. We tackle this problem by using a hypothesis-free machinelearning algorithm to estimate the transmission rate from data on non-pharmaceutical policies, and in turn forecast the confirmed cases using a mechanistic disease model. More specifically, we build a hybrid model consisting of a mechanistic ordinary differential equation (ODE) model and a gradient boosting model (GBM). To calibrate the parameters, we develop an “inverse method” that obtains the transmission rate inversely from the other variables in the ODE model and then feed it into the GBM to connect with the policy data. The resulting model forecasted the number of daily confirmed cases up to 35 days in the future in the United States with an averaged mean absolute percentage error of 27%. It can identify the most informative predictive variables, which can be helpful in designing improved forecasters as well as informing policymakers.
dc.identifier.doihttps://doi.org/10.7939/r3-mgd7-fy05
dc.language.isoen
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.subjectHypothesis-free
dc.subjectNon-pharmaceutical policies
dc.subjectCOVID-19
dc.subjectInverse method
dc.subjectMachine Learning
dc.subjectGeneralized boosting model
dc.titleA hypothesis-free bridging of disease dynamics and non-pharmaceutical policies
dc.typehttp://purl.org/coar/resource_type/c_6501 http://purl.org/coar/version/c_b1a7d7d4d402bcce http://purl.org/coar/version/c_71e4c1898caa6e32
ual.jupiterAccesshttp://terms.library.ualberta.ca/public

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