A comprehensive analysis of non-pharmaceutical interventions and vaccination on Ebolavirus disease outbreak: Stochastic modeling approach

TL;DR

This study uses a stochastic model to show how non-pharmaceutical interventions and vaccination can reduce the spread of Ebolavirus disease during outbreaks.

Contribution

The study introduces a stochastic model incorporating healthcare workers and unreported cases to evaluate NPIs and vaccination effects on EVD outbreaks.

Findings

Non-pharmaceutical interventions reduced transmission rate and infectious period by 30% and 40%, respectively.

Prompt outbreak detection could reduce NPI efforts by 45% compared to delayed recognition.

Vaccination of 20% of the population could reduce required NPIs by 40%.

Abstract

Ebolavirus disease (EVD) outbreaks have intermittently occurred since the first documented case in the 1970s. Due to its transmission characteristics, large outbreaks have not been observed outside Africa. However, within the continent, significant outbreaks have been attributed to factors such as endemic diseases with similar symptoms and inadequate medical infrastructure, which complicate timely diagnosis. In this study, we employed a stochastic modeling approach to analyze the spread of EVD during the early stages of an outbreak, with an emphasis on inherent risks. We developed a model that considers healthcare workers and unreported cases, and assessed the effect of non-pharmaceutical interventions (NPIs) using actual data. Our results indicate that the implementation of NPIs led to a decrease in the transmission rate and infectious period by 30% and 40% respectively, following the declaration of the outbreak. We also investigated the risks associated with delayed outbreak recognition. Our simulations suggest that, when accounting for NPIs and recognition delays, prompt detection could have resulted in a similar outbreak scale, with approximately 50% of the baseline NPIs effect. Finally, we discussed the potential effects of a vaccination strategy as a follow-up measure after the outbreak declaration. Our findings suggest that a vaccination strategy can reduce both the burden of NPIs and the scale of the outbreak.

Our research employs a stochastic model to analyze the early-stage spread of Ebolavirus Disease. We incorporated factors such as healthcare workers and unreported cases, and utilized real data to evaluate the impact of non-pharmaceutical interventions on disease transmission. Our findings indicate that rapid outbreak recognition could effectively control disease spread with reduced efforts. For example, if outbreak recognition occurred just one day after the first death, the level of non-pharmaceutical intervention required would have been 45% less than the baseline scenario, causing the same outbreak scale. On the contrary, a one-week delay in outbreak recognition required 50% higher non-pharmaceutical interventions. Furthermore, we explored the potential implementation of a vaccination strategy following an outbreak declaration. The vaccine administration, similar to early outbreak recognition, could reduce the burden of non-pharmaceutical interventions. In the model simulation, if 20% of the considered population (100,000) were vaccinated within 50 days after outbreak recognition, the level of non-pharmaceutical intervention required could be reduced by 40%. Our results suggest that such a strategy could mitigate both the scale of the outbreak and the necessity for additional interventions.

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