Description
Emerging Infectious Disease (EID) events pose significant threats to public health,the global economy, and human activities. Recent EIDs, such as SARS, H1N1 influenza,
and COVID-19, have resulted in millions of excess deaths worldwide and
substantial economic losses. Given that the most EIDs are zoonotic, and the unclear
influence of wildlife trade on the EIDs event with increasing human-animal contact.
This study investigates the influence of wildlife trade on EIDs after adjusting for the
effects of socio-economic factors, meteorological factors, geographical factors, and
biological factors under Bayesian framework. The data is principally available from
CITES, EIDR and Word Bank during 1975-2013.
Prior to model analysis, we first adjusted EIDs using reporting efforts across countries.
Global spatial autocorrelation and temporal autocorrelation of the adjusted
EIDs are analyzed using Moran’s I and ACF plots, respectively. After that, the
Bayesian zero-inflation joint model is constructed to analyze the zero-inflated EIDs
with using the faster INLA algorithm compared to MCMC. Penalized complexity
prior distribution is employed for the hyper-parameters involved in the random effects
for the trade-off of model bias and variance. The optimal model based on
common Bayesian criteria indicates that an increase in import wildlife trade activities
significantly elevates the likelihood of adjusted EIDs but does not impact its
intensity given its occurrence. Furthermore, GDP and precipitation exhibit a significant
negative association, while population density, population growth rate, and
mammal richness show a significant positive association with adjusted EIDs.
| Period | 2023 |
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