article · Epidemics
An analysis of the 2018 to 2019 Ebola virus disease outbreak in the Democratic Republic of the Congo demonstrates the use of non-parametrically estimated Hawkes point processes for real-time epidemiological forecasting. Using daily case records reported by local health authorities and verified by the World Health Organisation, the method generated short-term prospective projections over three-, six-, and nine-week horizons. The model predicted cumulative outbreak totals with measurable accuracy across these intervals. Projections across three and six weeks showed greater reliability, yielding median forecast errors under five cases per day, whereas nine-week estimates experienced some degradation with an error of 6.73 cases per day. The approach presents an easily applied statistical tool capable of producing timely, probabilistic outbreak estimates to support ongoing operational decisions and resource distribution during public health emergencies in complex settings.
Epidemics in crisis-affected regions require rapid, accurate forecasting to distribute medical supplies, deploy health workers, and coordinate interventions effectively. Evaluating straightforward statistical models using real outbreak data shows that reliable short-term predictions can be achieved with modest analytical complexity, offering health authorities practical guidance for managing emerging infectious disease crises under volatile field conditions.
This methodology is applied and tested using real-world public health surveillance data, positioning it as an analytical tool for public health agencies, emergency response planners, and humanitarian organisations. While primarily suited for public-sector and humanitarian use rather than a standalone commercial product, the underlying model could be incorporated into disease surveillance software to assist decision-makers with near real-time logistical planning and resource allocation.
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As of June 16, 2019, an Ebola virus disease (EVD) outbreak has led to 2136 reported cases in the northeastern region of the Democratic Republic of the Congo (DRC). As this outbreak continues to threaten the lives and livelihoods of people already suffering from civil strife and armed conflict, relatively simple mathematical models and their short-term predictions have the potential to inform Ebola response efforts in real time. We applied recently developed non-parametrically estimated Hawkes point processes to model the expected cumulative case count using daily case counts from May 3, 2018, to June 16, 2019, initially reported by the Ministry of Health of DRC and later confirmed in World Health Organization situation reports. We generated probabilistic estimates of the ongoing EVD outbreak in DRC extending both before and after June 16, 2019, and evaluated their accuracy by comparing forecasted vs. actual outbreak sizes, out-of-sample log-likelihood scores and the error per day in the median forecast. The median estimated outbreak sizes for the prospective thee-, six-, and nine-week projections made using data up to June 16, 2019, were, respectively, 2317 (95% PI: 2222, 2464); 2440 (95% PI: 2250, 2790); and 2544 (95% PI: 2273, 3205). The nine-week projection experienced some degradation with a daily error in the median forecast of 6.73 cases, while the six- and three-week projections were more reliable, with corresponding errors of 4.96 and 4.85 cases per day, respectively. Our findings suggest the Hawkes point process may serve as an easily-applied statistical model to predict EVD outbreak trajectories in near real-time to better inform decision-making and resource allocation during Ebola response efforts.
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DOI: 10.1016/j.epidem.2019.100354
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