Seasonal transmission potential and activity peaks of the new influenza A(H1N1): a Monte Carlo likelihood analysis based on human mobility

TL;DR

This study uses a global mobility model and likelihood analysis to estimate the transmission potential, seasonality, and peak activity times of the 2009 H1N1 influenza pandemic, providing insights for mitigation strategies.

Contribution

It introduces a Monte Carlo likelihood framework integrating mobility data to estimate pandemic parameters and seasonality effects for H1N1.

Findings

Estimated R0 = 1.75 for H1N1

Identified potential early peak in October/November in the Northern Hemisphere

Highlighted the impact of antiviral treatments on epidemic timing

Abstract

On 11 June the World Health Organization officially raised the phase of pandemic alert (with regard to the new H1N1 influenza strain) to level 6. We use a global structured metapopulation model integrating mobility and transportation data worldwide in order to estimate the transmission potential and the relevant model parameters we used the data on the chronology of the 2009 novel influenza A(H1N1). The method is based on the maximum likelihood analysis of the arrival time distribution generated by the model in 12 countries seeded by Mexico by using 1M computationally simulated epidemics. An extended chronology including 93 countries worldwide seeded before 18 June was used to ascertain the seasonality effects. We found the best estimate R0 = 1.75 (95% CI 1.64 to 1.88) for the basic reproductive number. Correlation analysis allows the selection of the most probable seasonal behavior based on the observed pattern, leading to the identification of plausible scenarios for the future unfolding of the pandemic and the estimate of pandemic activity peaks in the different hemispheres. We provide estimates for the number of hospitalizations and the attack rate for the next wave as well as an extensive sensitivity analysis on the disease parameter values. We also studied the effect of systematic therapeutic use of antiviral drugs on the epidemic timeline. The analysis shows the potential for an early epidemic peak occurring in October/November in the Northern hemisphere, likely before large-scale vaccination campaigns could be carried out. We suggest that the planning of additional mitigation policies such as systematic antiviral treatments might be the key to delay the activity peak inorder to restore the effectiveness of the vaccination programs.