A novel approach to estimating Rt through infection networks: understanding regional transmission dynamics of COVID-19

Incorporating infection networks into <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mrow><mml:mrow><mml:mi>R</mml:mi></mml:mrow> </mml:mrow> <mml:mrow><mml:mi>t</mml:mi></mml:mrow> </mml:msub> </mml:mrow> </mml:math> estimation thus provides decision-makers with timely insights for targeted interventions. Empirically reconstructing infection networks and directly estimating <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mrow><mml:mrow><mml:mi>R</mml:mi></mml:mrow> </mml:mrow> <mml:mrow><mml:mi>t</mml:mi></mml:mrow> </mml:msub> </mml:mrow> </mml:math> reveal real-time transmission dynamics often overlooked by aggregated approaches. This method can significantly improve outbreak forecasts, inform more precise public health policies, and strengthen pandemic preparedness.