Monkeypox (MPX), a zoonotic infection caused by the MPX virus (MPXV), traditionally occurred primarily in West and Central Africa. However, a dramatic global spread occurred in 2022, triggering the first multicountry outbreak outside Africa, becoming a global health emergency. This unprecedented outbreak necessitates a reassessment of MPX risk factors, considering the ongoing COVID-19 pandemic. The 2022 outbreak exhibited distinct characteristics, including sustained human-to-human transmission and a disproportionate impact on men who have sex with men (MSM). The post-COVID-19 era, with the easing of travel restrictions, further complicates the epidemiological landscape. Therefore, a comprehensive analysis is crucial to understand the evolving transmission patterns, identify key risk factors, and predict future trends. This study investigates the shifting risk factors for MPX infection, incorporating multidimensional characteristics potentially influencing its spread, ultimately aiming to inform the development of effective response strategies in the context of the COVID-19 pandemic.

Existing literature on MPX predominantly focuses on outbreaks in endemic regions in Africa. Previous studies identified risk factors such as HIV infection, population density, and age. However, the 2022 outbreak showed a different epidemiological picture. While some past studies explored the relationship between MPX transmission and air travel, they were limited in scope. Additionally, there's a lack of comprehensive analyses examining the interplay between MPX and COVID-19, and the influence of socioeconomic factors and human mobility on the global spread. This study aims to bridge these gaps by synthesizing existing knowledge and incorporating novel data to assess the current MPX situation.

This longitudinal study employed a multi-faceted approach to assess MPX risk factors and predict epidemiological trends. First, a comprehensive dataset of MPX cases from 1970 to 2022 was compiled from various sources, including GIDEON and the Global Health team. Twenty-one potential risk factors across seven dimensions (socioeconomic, biodiversity, environmental, health burden, behavioral, health services, and mobility) were considered. Correlation-based network analysis and multivariate regression models were used to identify significant associations between MPX infections and these risk factors, comparing historical data (2001-2021) with the 2022 outbreak. A modified Susceptible-Exposed-Infectious-Removed (SEIR) model, incorporating international flight arrival dynamics and susceptible population age distribution, was developed to forecast epidemic trends across six continents. K-means clustering analysis, driven by key risk factors, was used to classify countries into risk clusters based on their epidemiological profiles. Statistical analyses included Spearman's rank correlation, negative binomial regression, and the calculation of the basic reproduction number (R0).

The study revealed a significant shift in the key risk factors for MPX infection between the historical period (2001-2021) and the 2022 outbreak. Historically, HIV infection and population density were the dominant factors. However, the 2022 outbreak highlighted the crucial role of human mobility, particularly international flight arrivals, and the interaction with COVID-19 infections. Other significant factors included MSM population size, socioeconomic factors, and the capacity of national health systems. The network analysis revealed a complex interplay between various risk factors, with international flight arrivals and COVID-19 infection showing the strongest association with MPX infections after adjusting for confounding factors. The modified SEIR model accurately predicted the epidemic trends, revealing high R0 values in the early stage (before June 23, 2022) and a subsequent decline due to public health interventions. The model projected that Northern America and Latin America might surpass Europe in cumulative cases by the autumn of 2022. K-means clustering identified three distinct risk clusters of countries, reflecting varying levels of risk based on the identified risk factors. High-risk countries were characterized by high mobility, socioeconomic level, large MSM populations, high COVID-19 infections, and strong health care systems. Low-risk countries showed the opposite characteristics.

The findings highlight the dynamic nature of MPX risk factors, emphasizing the need for adaptive and flexible strategies in outbreak response. The strong association between international flight arrivals and MPX infections underscores the importance of strengthening global surveillance and travel health measures. The interaction between COVID-19 and MPX warrants further investigation into potential shared pathways, such as altered behavioral patterns or immune status. The high R0 values in the initial phase emphasize the rapid spread potential of MPX, while the subsequent decline indicates the effectiveness of public health interventions. The identified risk clusters provide valuable information for targeted resource allocation and public health interventions. The study’s prediction of Northern America and Latin America becoming high-risk regions should prompt proactive preparedness measures.

This study provides crucial insights into the changing landscape of MPX risk factors and future trends. The identified key risk factors, combined with the predictive power of the modified SEIR model and risk cluster classification, offer valuable guidance for global outbreak preparedness and response. Future research should focus on investigating the mechanisms underlying the interaction between COVID-19 and MPX and further refining the SEIR model to encompass additional factors that influence the spread of MPX.

The study relies on data from multiple sources, potentially introducing some heterogeneity and bias. The accuracy of COVID-19 case counts may be affected by information bias, particularly in the post-COVID-19 era. The analysis is based on country-level data, limiting the ability to draw inferences at the individual level. The SEIR model, while comprehensive, could be further improved by incorporating additional factors, such as vaccination coverage, as data become available. The study’s focus on imported cases limits insights into endemic regions.