Real-time forecasting the trajectory of monkeypox outbreaks at the national and global levels, July–October 2022

On May 7th, 2022, a case of monkeypox (mpox) with recent travel to Nigeria was reported in England [1, 2]. Shortly after, the Centers for Disease Control and Prevention (CDC) identified a case of monkeypox in Massachusetts on May 15th, 2022 [3]. Since then, multiple nations, including the U.S., have reported a surge in monkeypox cases, mostly among males within the communities of gay, bisexual, and other men who have sex with men (MSM) [4‐6]. The World Health Organization (WHO) declared monkeypox a global health emergency on July 23rd, 2022 [7]. As of November 23rd, 2022, over 79,900 monkeypox cases have been reported in non-endemic countries, especially in the USA, Spain, and Brazil, during the ongoing outbreak [8]. Given that this is an emerging infection in non-endemic countries, with little historical information about how outbreaks might unfold, mathematical models can help generate real-time forecasts of the trajectory of the epidemics and guide public health measures appropriate for a given geographic setting.

Monkeypox is an endemic zoonotic virus in Africa, most similar in clinical presentation to the Variola (Smallpox) virus [9]. Both are part of the Orthopoxvirus genus, which includes other viruses such as cowpox and Vaccinia virus—used in smallpox vaccines [9, 10]. Monkeypox symptoms include, but are not limited to, flu-like symptoms followed by a raised rash on the face and extremities. The incubation period is usually 6–13 days, with a symptomatic period ranging from 2 to 4 weeks [9]. Fortunately, monkeypox is not an airborne pathogen. Instead, transmission is mainly driven by prolonged close contact with infected individuals or direct contact with skin lesions, respiratory secretions, or recently contaminated objects—a feature that may facilitate control through basic public health measures [9].

The ability to forecast country-specific epidemic trajectories is particularly useful in an outbreak like this, which paints a unique epidemiological picture compared to past outbreaks within both endemic and non-endemic countries [4, 6, 11, 12]. For instance, sexual and intimate contact, specifically between men, has driven the great majority of infections [5, 13, 14]. Likewise, over 98% of cases in the USA and Spain are male, and most have identified as MSM [13, 15]. Cases of the ongoing outbreak are less likely to report prodromal symptoms, and rash occurs most frequently in the genital region [4, 13]. The scale of community spread is unprecedented [16].

While much has been learned about the epidemiology of this emerging outbreak during the last few months, substantial uncertainties remain about the effect of several variables on the epidemic trajectory, including the frequency and role of asymptomatic individuals, the role of pre-existing immunity from previous smallpox immunization campaigns, and the efficacy of available vaccines [17]. In this context, semi-mechanistic growth models are especially suitable for conducting short-term forecasts to guide response efforts and evaluate the impact of control measures, including behavior changes that mitigate transmission rates, contact tracing, and vaccination, on growth trends [18]. Previous real-time forecasting studies have employed a variety of mathematical models in the context of influenza, SARS, Ebola, and COVID-19 [19‐23]. Here, we employ an ensemble sub-epidemic modeling framework to characterize epidemic trajectories that result from sub-epidemics aggregation through an optimization process [19]. This framework has yielded competitive performance in short-term forecasts of various infectious disease outbreaks [19, 23]. In this study, we generate 4-week ahead forecasts of laboratory-confirmed cases of monkeypox in near real-time at the global level and for nations that have reported the great majority of the cases: Brazil, Canada, England, France, Germany, Spain, and the USA. We also evaluate the model fit and performance of the forecasts based on the mean absolute error (MAE), mean square error (MSE), 95% prediction interval coverage (PI), and weighted interval score (WIS).

We report results from short-term (4 weeks ahead) forecasts of monkeypox cases using a sub-epidemic modeling framework for the world and seven countries that, at the time this study began, had reported the great majority of cases. Our forecasts continue to support an overall declining trend in the number of new cases of monkeypox at the global and country-specific levels. Based on the top-ranked model and weighted ensemble model, we predict that during the next 4 weeks (the week of October 20th, 2022, through the week of November 10, 2022), a total 6232 (95% PI 487.8, 12,468.0, and 95% PI 492.8, 12,463.1) cases of monkeypox could be added globally. At the country level, our top-ranked model indicates that the highest number of new cases will be reported in the USA (OWID data) (median 1806, 95% PI 0, 5545) followed by Brazil (median 879, 95% PI 25, 1952) and Spain (median 43, 95% PI 0, 316). Overall, our models have performed reasonably well across study areas. The top-ranked and weighted ensemble model outperformed other models on average in forecasting performance.Our results offer valuable information to policymakers to guide the continued allocation of resources and inform mitigation efforts. More broadly, findings suggest that the epidemic could be brought under near-complete control in some regions should public health measures continue to be sustained, especially among the high-risk groups [42‐44]. Indeed, a core group of higher-risk people is thought to disproportionately contribute to transmission and thereby sustain sexually transmitted infection (STI) epidemics. Monkeypox is inherently different from other STIs like HIV, which has a lifelong duration, or bacterial STIs, which can be acquired repeatedly. Cases may decline rapidly as immunity increases among core group members, either due to infection or vaccination. Without a core group driving the epidemic, monkeypox may become endemic with low transmission levels [45‐47]. The current monkeypox outbreak is unprecedented in size and geographic scope. As of November 23rd, 2022, a total of 110 countries globally have reported monkeypox cases at 80,899 [8]. Only seven countries have historically reported monkeypox cases indicating that more than 93% of the countries reporting cases are non-endemic to monkeypox [8]. Our latest short-term forecasts from top-ranked models and weighted ensemble models conducted in near real-time indicate a clear, continued slow down in the number of new cases globally and in each country included in the study. This mirrors the recent continental declines in cases reported for Europe and the Americas [43, 48, 49].

Findings support the significant impact of current measures to contain the outbreak in different areas. For example, in the USA, the primary strategy has been a combination of increasing education around monkeypox (e.g., symptoms, transmission), encouraging practices that reduce potential close contacts and increasing access to vaccination and testing for high-risk groups [50‐52]. Although supply-chain shortages impacted early vaccine access, as of August 26th, 2022, the availability of monkeypox vaccines had increased to sufficient levels to combat the outbreak. The racial disparities in access to vaccines that arose in late August are continuing to persist [53‐55] though some progress has been made in improving the monkeypox vaccination among racial and ethnic minority groups in the USA. For example, according to a recent morbidity and mortality weekly report (MMWR), between May 22–June 25 and July 31–October 10, 2022, the proportion of monkeypox vaccine recipients increased from 15 to 23% among Hispanic and from 6 to 13% among Black population [56]. In addition, although vaccines are more recently available, behavioral modification within high-risk groups appears to be driving declines in cases. Continuing these behaviors (e.g., limiting one-time sexual encounters) is crucial in slowing the transmission of monkeypox [5, 42, 57]. Based on the early evidence from Europe, the World Health Organization (WHO) is quite optimistic that the current outbreak of monkeypox can be contained with the improvement in vaccine supply chains in addition to early detection of the cases and educational interventions which lead to behavioral modifications in the high-risk groups [58]. Moreover, recent research indicates that the majority of monkeypox cases resulting in severe disease or death have been among MSM with compromised immune systems (e.g., due to untreated HIV infection). Specifically, increased burden has been noted among Black populations, and those experiencing mental health challenges or housing insecurities, reflecting the existing inequities in access to resources diagnosis, treatment, and prevention of monkeypox [59].

Our study is not exempt from limitations. First, our analysis relied on weekly time series data of lab-confirmed monkeypox cases from two sources, which display irregular daily reporting patterns [24, 25]. These sources use different approaches in compiling data and addressing data issues, which affect the characterization of the epidemic curve. For example, the CDC data uses cases with reporting data that includes either the positive laboratory test report date, CDC call center reporting date, or case data entry date into CDC’s emergency response common operating platform [24]. The OWID team uses laboratory-confirmed case data reported to the World Health Organization via WHO Member States [25, 60]. In addition, the data used for forecasting could also be underestimated due to delays between the date of testing and the date of reporting. Also, the weekly data used in our real-time forecasts has exhibited revisions that retrospectively adjusted the time series. Hence, significant increases or decreases may be observed in reported cases for the same date between forecasting periods. Indeed, the CDC acknowledged the presence of data adjustments within their posted data [61]. Similar issues have been noted in COVID-19 forecasting studies since ground truth data adjustments occurred during the pandemic [20]. Nevertheless, in the case of our study and the COVID-19 forecasting study, forecasts are being conducted in real time using ground truth data. Therefore, each weekly forecast is generated using the latest time series available on each prediction date whereas forecasts were scored using the most up-to-date data at the time of the study (week of October 13th, 2022). Because we are dealing with limited epidemic data in this study, we often examined forecasts derived from the second-ranked sub-epidemic model even when it yielded substantially diminished statistical support relative to the top-ranked model. The models employed in this study are semi-mechanistic in that they give insight into the nature of the process that generated the epidemic trends in terms of the aggregation of sub-epidemic trajectories. However, the models are not intended to quantify the effects of different factors, such as behavior change and vaccination, on the declining trend. Finally, it should be noted that our short-term forecasts are based on the inherent assumption that current behavior practices will not change substantially, at least over short time horizons. Further, our models are not sensitive to long-term forecasting episodic risk behaviors that are seasonal or event specific (e.g., LGBTQ Pride festivals). For example, a previous study has reported episodic risk behaviors among MSM, such as condomless anal sex with new male sex partners while vacationing [62].

In future work, we plan to systematically assess the forecasting performance of the models against other competing models, such as the Autoregressive Integrated Moving Average (ARIMA), which has been broadly applied to forecast time series of epidemics and various other phenomena such as the weather and the stock market [19, 63, 64]. During the COVID-19, this sub-epidemic modeling framework demonstrated reliable forecasting performance in 10- to 30-day ahead forecasts of daily deaths, outperforming ARIMA models in weekly short-term forecasts covering the U.S. trajectory of the COVID-19 pandemic from the early phase of spring 2020 to the Omicron-dominated wave [19].

In summary, real-time forecasting during epidemic emergencies offers actionable information that governments can use to anticipate healthcare needs and strategize the intensity and configuration of public health interventions. Our study provides and assesses near-real time short-term forecasts of monkeypox cases globally and for seven countries that have reported a higher number of cases for a large part of the epidemic. Our models continue to predict a slowdown in the incidence of monkeypox both at the global and country levels. Overall, our models have demonstrated utility in providing short-term forecasts, capturing the growth rate slowdown and peak timing with reasonable accuracy. Our findings likely reflect the impact of increased immunity and behavioral modification among high-risk populations.