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

SubEpiPredict: A tutorial-based primer and toolbox for fitting and forecasting growth trajectories using the ensemble n-sub-epidemic modeling framework

An ensemble n-sub-epidemic modeling framework that integrates sub-epidemics to capture complex temporal dynamics has demonstrated powerful forecasting capability in previous works. This modeling framework can characterize complex epidemic patterns, including plateaus, epidemic resurgences, and epidemic waves characterized by multiple peaks of different sizes. In this tutorial paper, we introduce and illustrate SubEpiPredict, a user-friendly MATLAB toolbox for fitting and forecasting time series data using an ensemble n-sub-epidemic modeling framework. The toolbox can be used for model fitting, forecasting, and evaluation of model performance of the calibration and forecasting periods using metrics such as the weighted interval score (WIS). We also provide a detailed description of these methods including the concept of the n-sub-epidemic model, constructing ensemble forecasts from the top-ranking models, etc. For the illustration of the toolbox, we utilize publicly available daily COVID-19 death data at the national level for the United States. The MATLAB toolbox introduced in this paper can be very useful for a wider group of audiences, including policymakers, and can be easily utilized by those without extensive coding and modeling backgrounds.

Mpox outbreak control indicators used in Ontario, Canada: May 21-December 10, 2022

Since May 2022, over 91 000 cases of mpox have been reported globally with the majority of cases occurring among adult males who identify as gay, bisexual, or men who have sex with men (gbMSM). Given the rapid emergence of the global mpox outbreak, many public health authorities did not have established mpox outbreak control indicators or criteria for declaring an mpox outbreak over. Expert consensus in Ontario, Canada, set thresholds for five key indicators of mpox outbreak control as follows: estimated number of currently infectious cases < 5; effective reproductive number < 1.0; doubling time > 42 days; weekly test positivity < 5%; and sporadic non-gbMSM cases (i.e., female and pediatric cases). Once all indicators were achieved, a 52-day period based on two incubation periods for mpox and a 10-day reporting delay was employed to monitor for indicator stability. After all five indicators remained at expected levels, the mpox outbreak in Ontario was declared over on December 10, 2022. Despite current low levels of mpox activity globally, some jurisdictions may benefit from utilizing or modifying these outbreak control indicators during a future localized mpox outbreak.

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

In May 2022, public health officials noted an unprecedented surge in mpox cases in non-endemic countries worldwide. As the epidemic accelerated, multi-model forecasts of the epidemic's trajectory were critical in guiding the implementation of public health interventions and determining policy. As the case levels have significantly decreased as of early September 2022, evaluating model performance is essential to advance the growing field of epidemic forecasting. Using laboratory-confirmed mpox case data from the Centers for Disease Control and Prevention (CDC) and Our World in Data (OWID) teams through the week of January 26th, 2023, we generated retrospective sequential weekly forecasts (e.g., 1-4-weeks) for Brazil, Canada, France, Germany, Spain, the United Kingdom, the USA, and at the global scale using models that require minimal input data including the auto-regressive integrated moving average (ARIMA), general additive model (GAM), simple linear regression (SLR), Facebook's Prophet model, as well as the sub-epidemic wave (spatial-wave) and n -sub-epidemic modeling frameworks. We assess forecast performance using average mean squared error (MSE), mean absolute error (MAE), weighted interval score (WIS), 95% prediction interval coverage (95% PI coverage), and skill scores. Average Winkler scores were used to calculate skill scores for 95% PI coverage. Overall, the n -sub-epidemic modeling framework outcompeted other models across most locations and forecasting horizons, with the unweighted ensemble model performing best across all forecasting horizons for most locations regarding average MSE, MAE, WIS, and 95% PI coverage. However, many locations had multiple models performing equally well for the average 95% PI coverage. The n -sub-epidemic and spatial-wave frameworks improved considerably in average MSE, MAE, and WIS, and Winkler scores (95% PI coverage) relative to the ARIMA model. Findings lend further support to sub-epidemic frameworks for short-term forecasting epidemics of emerging and re-emerging infectious diseases.

Data-driven estimation of the instantaneous reproduction number and growth rates for the 2022 monkeypox outbreak in Europe

OBJECTIVE

To estimate the instantaneous reproduction number Rt and the epidemic growth rates for the 2022 monkeypox outbreaks in the European region.

METHODS

We gathered daily laboratory-confirmed monkeypox cases in the most affected European countries from the beginning of the outbreak to September 23, 2022. A data-driven estimation of the instantaneous reproduction number is obtained using a novel filtering type Bayesian inference. A phenomenological growth model coupled with a Bayesian sequential approach to update forecasts over time is used to obtain time-dependent growth rates in several countries.

RESULTS

The instantaneous reproduction number Rt for the laboratory-confirmed monkeypox cases in Spain, France, Germany, the UK, the Netherlands, Portugal, and Italy. At the early phase of the outbreak, our estimation for Rt, which can be used as a proxy for the basic reproduction number R0, was 2.06 (95% CI 1.63 - 2.54) for Spain, 2.62 (95% CI 2.23 - 3.17) for France, 2.81 (95% CI 2.51 - 3.09) for Germany, 1.82 (95% CI 1.52 - 2.18) for the UK, 2.84 (95% CI 2.07 - 3.91) for the Netherlands, 1.13 (95% CI 0.99 - 1.32) for Portugal, 3.06 (95% CI 2.48 - 3.62) for Italy. Cumulative cases for these countries present subexponential rather than exponential growth dynamics.

CONCLUSIONS

Our findings suggest that the current monkeypox outbreaks present limited transmission chains of human-to-human secondary infection so the possibility of a huge pandemic is very low. Confirmed monkeypox cases are decreasing significantly in the European region, the decline might be attributed to public health interventions and behavioral changes in the population due to increased risk perception. Nevertheless, further strategies toward elimination are essential to avoid the subsequent evolution of the monkeypox virus that can result in new outbreaks.

Short-Term Forecasting of Monkeypox Cases Using a Novel Filtering and Combining Technique

In the modern world, new technologies such as artificial intelligence, machine learning, and big data are essential to support healthcare surveillance systems, especially for monitoring confirmed cases of monkeypox. The statistics of infected and uninfected people worldwide contribute to the growing number of publicly available datasets that can be used to predict early-stage confirmed cases of monkeypox through machine-learning models. Thus, this paper proposes a novel filtering and combination technique for accurate short-term forecasts of infected monkeypox cases. To this end, we first filter the original time series of the cumulative confirmed cases into two new subseries: the long-term trend series and residual series, using the two proposed and one benchmark filter. Then, we predict the filtered subseries using five standard machine learning models and all their possible combination models. Hence, we combine individual forecasting models directly to obtain a final forecast for newly infected cases one day ahead. Four mean errors and a statistical test are performed to verify the proposed methodology's performance. The experimental results show the efficiency and accuracy of the proposed forecasting methodology. To prove the superiority of the proposed approach, four different time series and five different machine learning models were included as benchmarks. The results of this comparison confirmed the dominance of the proposed method. Finally, based on the best combination model, we achieved a forecast of fourteen days (two weeks). This can help to understand the spread and lead to an understanding of the risk, which can be utilized to prevent further spread and enable timely and effective treatment.

A hybrid forecasting technique for infection and death from the mpox virus

Objectives

The rising of new cases and death counts from the mpox virus (MPV) is alarming. In order to mitigate the impact of the MPV it is essential to have information of the virus's future position using more precise time series and stochastic models. In this present study, a hybrid forecasting system has been developed for new cases and death counts for MPV infection using the world daily cumulative confirmed and death series.

Methods

The original cumulative series was decomposed into new two subseries, such as a trend component and a stochastic series using the Hodrick-Prescott filter. To assess the efficacy of the proposed models, a comparative analysis with several widely recognized benchmark models, including auto-regressive (AR) model, auto-regressive moving average (ARMA) model, non-parametric auto-regressive (NPAR) model and artificial neural network (ANN), was performed.

Results

The introduction of two novel hybrid models, HPF 1 1 and HPF 3 4 , which demonstrated superior performance compared to all other models, as evidenced by their remarkable results in key performance indicators such as root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE), is a significant advancement in disease prediction.

Conclusion

The new models developed can be implemented in forecasting other diseases in the future. To address the current situation effectively, governments and stakeholders must implement significant changes to ensure strict adherence to standard operating procedures (SOPs) by the public. Given the anticipated continuation of increasing trends in the coming days, these measures are essential for mitigating the impact of the outbreak.