Incorporating human mobility to enhance epidemic response and estimate real-time reproduction numbers
Mousumi Roy, Hannah E. Clapham, Swapnil Mishra, Denise Kühnert, Denise Kühnert, Denise Kühnert, Denise Kühnert
TL;DR
This paper introduces a new method to estimate real-time disease spread by incorporating human mobility data into epidemic models.
Contribution
The study introduces a mobility-integrated renewal equation framework for estimating real-time reproduction numbers across regions.
Findings
Ignoring human mobility leads to biased estimates of real-time reproduction numbers.
The mobility-integrated framework improves the accuracy of epidemic assessments at different spatial resolutions.
The approach uses publicly available datasets and is applicable to real-world scenarios like SARS-CoV-2.
Abstract
Human mobility plays a critical role in the transmission dynamics of infectious diseases, influencing both their spread and the effectiveness of control measures. In the process of quantifying the real-time situation of an epidemic, the instantaneous reproduction number Rt appears to be one of the useful metrics widely used by public health researchers, officials, and policy makers. Since individuals can contract infections both within their region of origin and in other regions they visit, ignoring human mobility in the estimation process overlooks its impact on transmission dynamics and can lead to biased estimates of Rt, potentially misrepresenting the true epidemic situation. Our study explicitly integrates human mobility into a renewal-equation based disease transmission model to capture the mobility-driven effect on transmission. By incorporating pathogen-specific generation-time…
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Taxonomy
TopicsCOVID-19 epidemiological studies · Zoonotic diseases and public health · Data-Driven Disease Surveillance