The role of vaccination and public awareness in forecasts of Mpox incidence in the United Kingdom

Abstract

Beginning in May 2022, Mpox virus spread rapidly in high-income countries through close human-to-human contact primarily amongst communities of gay, bisexual and men who have sex with men (GBMSM). Behavioural change arising from increased knowledge and health warnings may have reduced the rate of transmission and modified Vaccinia-based vaccination is likely to be an effective longer-term intervention. We investigate the UK epidemic presenting 26-week projections using a stochastic discrete-population transmission model which includes GBMSM status, rate of formation of new sexual partnerships, and clique partitioning of the population. The Mpox cases peaked in mid-July; our analysis is that the decline was due to decreased transmission rate per infected individual and infection-induced immunity among GBMSM, especially those with the highest rate of new partners. Vaccination did not cause Mpox incidence to turn over, however, we predict that a rebound in cases due to behaviour reversion was prevented by high-risk group-targeted vaccination.

Early underdetected dissemination across countries followed by extensive local transmission propelled the 2022 mpox epidemic

“…Similarly, modeling studies have found that behavioral modifications within the queer community was the main contributing factor to the slowing mpox spread in Washington D.C., although they also found that vaccination campaigns were ultimately needed to definitively curb the local epidemic and prevent future outbreaks (43,44). Additionally, a UK-based modeling study focusing on gay and bisexual men who have sex with men found that vaccination could not explain the drop in mpox incidence in the region, but rather attribute the declining incidence to changes in behavior within the same community (45). Together, these findings highlight the significant effect of behavioral change among men who have sex with men and other high-risk communities in curbing the epidemic as well as push for a prompt public health response that includes early vaccination campaigns in order to maximize the population-level effectiveness of vaccination campaigns.…”

Modelling the effectiveness of isolation strategies for managing mpox outbreaks with variable infectiousness profiles

“…Although growth of the outbreak was initially rapid, the global trend in reported cases changed around the summer of 2022 and has been declining ever since 2 . Recent studies have suggested that the case saturation in many countries may be explained largely by infection-derived immunity accumulated among individuals who have many sexual partners 3 , and the following decline in cases may have been accelerated by vaccination campaigns or behavioral changes 4,5 . However, since the beginning of 2023, sporadic outbreaks have been reported, mainly in Asian countries, which were least affected by the 2022 outbreak and where vaccination campaigns had not yet been initiated, leading to a substantial number of individuals remaining at risk of infection 6 .…”

Retrospective evaluation of short-term forecast performance of ensemble sub-epidemic frameworks and other time-series models: The 2022-2023 mpox outbreak across multiple geographical scales, July 14th, 2022, through February 26th, 2023

“…Several methodologies have been employed to forecast the case trajectory of the 2022-2023 mpox epidemic in various geographical regions, including, but not limited to, models focused on human judgment [29], deep learning, and artificial intelligence models (AI) [30][31][32], machine learning models [30][31][32][33][34][35], statistical models such as auto-regressive integrated moving average (ARIMA) models [31,[33][34][35][36][37], compartmental models [38], and semi-mechanistic sub-epidemic models [16]. Performance metrics employed have mainly included variations of mean absolute error (MAE) [16,30,31,[33][34][35]37], mean squared error (MSE) [16,[30][31][32][33][34][35][36][37], and mean absolute percentage error (MAPE) [16,31,[33][34][35], with only two studies also including probabilistic measures of performance such as the weighted interval score (WIS) [16,39].…”

“…Performance metrics employed have mainly included variations of mean absolute error (MAE) [16,30,31,[33][34][35]37], mean squared error (MSE) [16,[30][31][32][33][34][35][36][37], and mean absolute percentage error (MAPE) [16,31,[33][34][35], with only two studies also including probabilistic measures of performance such as the weighted interval score (WIS) [16,39]. Of the studies included here, four focused on the ascending phase of the epidemic (January 2022-Mid August 2022) [29,33,34,36], four evaluated forecasts of the ascending and peak of the epidemic (February 2022-September 2022) [30][31][32]39], and four focused on the ascending, peak and decline phases of the epidemic (January 2022-Present) [16,35,37,38].…”